* [22] Y. Rong, Y. Bian, T. Xu, W. Xie, Y. Wei, W. Huang, and J. Huang, “Self-supervised graph transformer on large-scale molecular data,” in _Proceedings of NIPS_ , ”2020”.
  * [23] G. Karypis and V. Kumar, “Multilevel k-way hypergraph partitioning,” _Journal of VLSI design_ , vol. 11, no. 3, pp. 285–300, 2000.
  * [24] D. Kempe, J. Kleinberg, and É. Tardos, “Maximizing the spread of influence through a social network,” in _Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining_ , 2003, pp. 137–146.
  * [25] X. Xu, N. Yuruk, Z. Feng, and T. A. Schweiger, “Scan: a structural clustering algorithm for networks,” in _Proceedings of SIGKDD_ , ”2007”, pp. 824–833.
  * [26] Y. Sun, J. Han, X. Yan, P. S. Yu, and T. Wu, “Pathsim: Meta path-based top-k similarity search in heterogeneous information networks,” vol. 4, no. 11, 2011, pp. 992–1003.
  * [27] J. J. McAuley and J. Leskovec, “Learning to discover social circles in ego networks,” in _Proceedings of NIPS_ , ”2012”, pp. 548–56.
  * [28] J. Tang, M. Qu, and Q. Mei, “Pte: Predictive text embedding through large-scale heterogeneous text networks,” in _Proceedings of KDD_ , ”2015”, pp. 1165–1174.
  * [29] S. Cao, W. Lu, and Q. Xu, “Grarep: Learning graph representations with global structural information,” in _Proceedings of CIKM_ , ”2015”, pp. 891–900.
  * [30] Z. Yang, W. Cohen, and R. Salakhudinov, “Revisiting semi-supervised learning with graph embeddings,” in _Proceedings of ICML_ , ”2016”, pp. 40–48.
  * [31] T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” _Journal of arXiv preprint arXiv:1609.02907_ , 2016\.
  * [32] T. N. Kipf and W. Max, “Variational graph auto-encoders,” _arXiv preprint arXiv:1611.07308_ , 2016.
  * [33] A. Grover and J. Leskovec, “Node2vec: Scalable feature learning for networks,” in _Proceedings of KDD_ , ”2016”, pp. 855–864.
  * [34] W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in _Proceedings of NIPS_ , ”2017”, pp. 1025–1035.
  * [35] J. Chen, T. Ma, and C. Xiao, “Fastgcn: fast learning with graph convolutional networks via importance sampling,” in _Proceedings of ICLR_ , ”2018”.
  * [36] P. Veliˇckovi´c, G. Cucurull, A. Casanova, A. Romero, P. Li‘o, and Y. Bengio, “Graph attention networks,” _Journal of arXiv preprint arXiv:1710.10903_ , 2017.
  * [37] K. Tu, P. Cui, X. Wang, F. Wang, and W. Zhu, “Structural deep embedding for hyper-networks,” in _Proceedings of he AAAI Conference on Artificial Intelligence_ , 2018.
  * [38] Y. Zhou, S. Wu, C. Jiang, Z. Zhang, D. Dou, R. Jin, and P. Wang, “Density-adaptive local edge representation learning with generative adversarial network multi-label edge classification,” in _Proceedings of ICDM_ , 2018, pp. 1464–1469.
  * [39] X. Wang, H. Ji, C. Shi, B. Wang, P. Cui, P. Yu, and Y. Ye, “Heterogeneous graph attention network,” in _Proceddings of WWW_ , ”2019”, pp. 2022–2032.
  * [40] J. Lee, I. Lee, and J. Kang, “Self-attention graph pooling,” in _Proceedings of ICML_ , ”2019”.
  * [41] A. Sankar, Y. Wu, L. Gou, W. Zhang, and H. Yang, “Dysat: Deep neural representation learning on dynamic graphs via self-attention networks,” in _Proceedings of the 13th international conference on web search and data mining_ , 2020, pp. 519–527.
  * [42] J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra, “Beyond homophily in graph neural networks: Current limitations and effective designs,” _Advances in Neural Information Processing Systems_ , vol. 33, pp. 7793–7804, 2020.
  * [43] J. Wu, X. Wang, F. Feng, X. He, L. Chen, J. Lian, and X. Xie, “Self-supervised graph learning for recommendation,” in _Proceedings of SIGIR_ , ”2021”, pp. 726–735.
  * [44] E. Meirom, H. Maron, S. Mannor, and G. Chechik, “Controlling graph dynamics with reinforcement learning and graph neural networks,” in _Proceedings of ICML_. PMLR, ”2021”, pp. 7565–7577.
  * [45] S. Chen, P.-L. Guhur, M. Tapaswi, C. Schmid, and I. Laptev, “Think global, act local: Dual-scale graph transformer for vision-and-language navigation,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2022, pp. 16 537–16 547.
  * [46] N. Keriven, “Not too little, not too much: a theoretical analysis of graph (over) smoothing,” _arXiv preprint arXiv:2205.12156_ , 2022.
  * [47] J. Peng, Z. Chen, Y. Shao, Y. Shen, L. Chen, and J. Cao, “Sancus: sta le n ess-aware c omm u nication-avoiding full-graph decentralized training in large-scale graph neural networks,” _Proceedings of the VLDB Endowment_ , vol. 15, no. 9, pp. 1937–1950, 2022.
  * [48] J. Ma, M. Wan, L. Yang, J. Li, B. Hecht, and J. Teevan, “Learning causal effects on hypergraphs,” in _Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_ , 2022, pp. 1202–1212.
  * [49] J. Tang, H. Gao, X. Hu, and H. Liu, “Exploiting homophily effect for trust prediction,” in _Proceedings of WSDM_ , 2013, pp. 53–62.
  * [50] G. Kossinets and D. J. Watts, “Origins of homophily in an evolving social network,” _American journal of sociology_ , vol. 115, no. 2, pp. 405–450, 2009.
  * [51] Z. Yan, T. Ma, L. Gao, Z. Tang, Y. Wang, and C. Chen, “Neural approximation of extended persistent homology on graphs,” _arXiv preprint arXiv:2201.12032_ , 2022.
  * [52] O. Barranco, C. Lozares, and D. Muntanyola-Saura, “Heterophily in social groups formation: a social network analysis,” _Quality & Quantity_, vol. 53, no. 2, pp. 599–619, 2019.
  * [53] Y. Yan, M. Hashemi, K. Swersky, Y. Yang, and D. Koutra, “Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks,” _arXiv preprint arXiv:2102.06462_ , 2021.
  * [54] T. Xiao, Z. Chen, Z. Guo, Z. Zhuang, and S. Wang, “Decoupled self-supervised learning for non-homophilous graphs,” _arXiv preprint arXiv:2206.03601_ , 2022.
  * [55] D. Kimura and Y. Hayakawa, “Coevolutionary networks with homophily and heterophily,” _Physical Review E_ , vol. 78, no. 1, p. 016103, 2008.
  * [56] Y. Liu, X. Ao, F. Feng, and Q. He, “Ud-gnn: Uncertainty-aware debiased training on semi-homophilous graphs,” in _Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_ , 2022, pp. 1131–1140.
  * [57] S. Chanpuriya and C. Musco, “Simplified graph convolution with heterophily,” _arXiv preprint arXiv:2202.04139_ , 2022.
  * [58] S. Suresh, V. Budde, J. Neville, P. Li, and J. Ma, “Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns,” _arXiv preprint arXiv:2106.06586_ , 2021.
  * [59] J. Zhu, J. Jin, D. Loveland, M. T. Schaub, and D. Koutra, “How does heterophily impact the robustness of graph neural networks? theoretical connections and practical implications,” in _Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_ , 2022, pp. 2637–2647.
  * [60] L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: Bringing order to the web,” Stanford InfoLab, Tech. Rep., 1999.
  * [61] D. He, Y. Song, D. Jin, Z. Feng, B. Zhang, Z. Yu, and W. Zhang, “Community-centric graph convolutional network for unsupervised community detection,” in _Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on artificial intelligence_ , 2021, pp. 3515–3521.
  * [62] S. Tao, Q. Cao, H. Shen, J. Huang, Y. Wu, and X. Cheng, “Single node injection attack against graph neural networks,” in _Proceedings of the 30th ACM International Conference on Information & Knowledge Management_, ”2021”, pp. 1794–1803.
  * [63] C. Li, K. Jia, D. Shen, C.-J. R. Shi, and H. Yang, “Hierarchical representation learning for bipartite graphs.” in _IJCAI_ , vol. 19, 2019, pp. 2873–2879.
  * [64] Z. Li, X. Shen, Y. Jiao, X. Pan, P. Zou, X. Meng, C. Yao, and J. Bu, “Hierarchical bipartite graph neural networks: Towards large-scale e-commerce applications,” in _2020 IEEE 36th International Conference on Data Engineering (ICDE)_. IEEE, ”2020”, pp. 1677–1688.
  * [65] M. Nassar, “Hierarchical bipartite graph convolution networks,” _arXiv preprint arXiv:1812.03813_ , 2018.
  * [66] Y. Zhao, S. Wei, H. Du, X. Chen, Q. Li, F. Zhuang, J. Liu, and G. Kou, “Learning bi-typed multi-relational heterogeneous graph via dual hierarchical attention networks,” _IEEE Transactions on Knowledge & Data Engineering_, no. 01, pp. 1–12, 2022.
  * [67] T. PENG, N.-y. YANG, Y.-b. XU, B.-b. WANG, and L. LIU, “An outlier detection method based on ranking and clustering in bi-typed heterogeneous network,” _ACTA ELECTONICA SINICA_ , vol. 46, no. 2, p. 281, 2018.
  * [68] Y. Zhao, H. Du, Y. Liu, S. Wei, X. Chen, H. Feng, Q. Shuai, Q. Li, F. Zhuang, and G. Kou, “Stock movement prediction based on bi-typed and hybrid-relational market knowledge graph via dual attention networks,” _arXiv preprint arXiv:2201.04965_ , 2022.
  * [69] Y. Sun, Y. Yu, and J. Han, “Ranking-based clustering of heterogeneous information networks with star network schema,” in _Proceedings of KDD_ , ”2009”, pp. 797–806.
  * [70] J. Wu, Q. Zhang, L. Yu, W. Ma, Y. Wu, S. Deng, and H. Huang, “Multi-typed community discovery in dynamic heterogeneous information networks through tensor method,” in _2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE)_. IEEE, 2017, pp. 59–65.
  * [71] C. Meng, R. Cheng, S. Maniu, P. Senellart, and W. Zhang, “Discovering meta-paths in large heterogeneous information networks,” in _Proceedings of WWW_ , 2015, pp. 754–764.
  * [72] T. yang Fu, W.-C. Lee, and Z. Lei, “Hin2vec: Explore metapaths in heterogeneous information networks for representation learning,” in _Proceddings of CIKM_ , 2017, pp. 1797–1806.
  * [73] G. Wan, B. Du, S. Pan, and G. Haffari, “Reinforcement learning based meta-path discovery in large-scale heterogeneous information networks,” in _Proceedings of the AAAI_ , ”2020”, pp. 6094–6101.
  * [74] Y. Sun, B. Norick, J. Han, X. Yan, P. S. Yu, and X. Yu, “Integrating meta-path selection with user-guided object clustering in heterogeneous information networks,” _Journal of ACM Transactions on Knowledge Discovery from Data_ , vol. 7, no. 3, pp. 1348–1356, 2013.
  * [75] K. Yang, J. Zhu, and X. Guo, “Poi neural-rec model via graph embedding representation,” _Tsinghua Science and Technology_ , vol. 26, no. 2, pp. 208–218, 2020.
  * [76] W. Ellens and R. EKooij, “Graph measures and network robustness,” _Journal of arXiv preprint arXiv:1311.5064_ , 2013.
  * [77] S. Baswana and S. Sen, “Approximate distance oracles for unweighted graphs in expected o (n 2) time,” _Journal of ACM Transactions on Algorithms (TALG)_ , vol. 2, no. 4, pp. 557–577, 2006.
  * [78] G. L. Miller, R. Peng, and S. C. Xu, “Parallel graph decompositions using random shifts,” in _Proceedings of ACM_ , 2013, pp. 196–203.
  * [79] K. Wang, J. An, M. Zhou, Z. Shi, X. Shi, and Q. Kang, “Minority-weighted graph neural network for imbalanced node classification in social networks of internet of people,” _IEEE Internet of Things Journal_ , 2022.
  * [80] W. Zhou, H. Huang, Q.-S. Hua, D. Yu, H. Jin, and X. Fu, “Core decomposition and maintenance in weighted graph,” _Journal of World Wide Web_ , vol. 24, no. 2, pp. 541–561, 2021.
  * [81] M. Mao, J. Lu, J. Han, and G. Zhang, “Multiobjective e-commerce recommendations based on hypergraph ranking,” _Information Sciences_ , vol. 471, pp. 269–287, 2019.
  * [82] S. Umeyama, “An eigendecomposition approach to weighted graph matching problems,” _Proceedings of IEEE_ , vol. 10, no. 5, pp. 695–703, 1988.
  * [83] I. S. Dhillon, Y. Guan, and B. Kulis, “Weighted graph cuts without eigenvectors a multilevel approach,” _Journal of IEEE transactions on pattern analysis and machine intelligence_ , vol. 29, no. 11, pp. 1944––1957, 2007.
  * [84] F. Wu, T. Zhang, A. H. de Souza Jr., C. Fifty, T. Yu, and K. Q. Weinberger, “Simplifying graph convolutional networks,” in _Proceddings of ICML_ , ”2019”, p. 6861–6871.
  * [85] H. Toivonen, F. Zhou, A. Hartikainen, and A. Hinkka, “Compression of weighted graphs,” in _Proceedings of SIGKDD_ , ”2011”, pp. 965–973.
  * [86] J. Bruna and X. Li, “Community detection with graph neural networks,” _stat_ , vol. 1050, p. 27, 2017.
  * [87] L. Yang, X. Cao, D. Jin, X. Wang, and D. Meng, “A unified semi-supervised community detection framework using latent space graph regularization,” _IEEE transactions on cybernetics_ , vol. 45, no. 11, pp. 2585–2598, 2014\.
  * [88] Z. Xiao, H. Wen, A. Markham, and N. Trigoni, “Indoor tracking using undirected graphical models,” _IEEE Transactions on Mobile Computing_ , vol. 14, no. 11, pp. 2286–2301, 2015.
  * [89] B. Fu, S. Fu, L. Wang, Y. Dong, and Y. Ren, “Deep residual split directed graph convolutional neural networks for action recognition,” _Journal of IEEE MultiMedia_ , vol. 27, no. 4, pp. 9–17, 2020.
  * [90] F. Scarselli, M. Gori, A. C. Tsoi, and G. Monfardini, “The graph neural network model,” _Journal of IEEE transactions on neural networks_ , vol. 20, no. 1, pp. 61–80, 2008.
  * [91] Y. Sun, C. C. Aggarwal, and J. Han, “Relation strength-aware clustering of heterogeneous information networks with incomplete attributes,” _Journal of arXiv preprint arXiv:1201.6563_ , 2012.
  * [92] P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, “Collective classification in network data,” _Journal of AI magazine_ , vol. 29, no. 3, p. 93, 2008.
  * [93] S. J. Yoo, “Distributed consensus tracking for multiple uncertain nonlinear strict-feedback systems under a directed graph,” _Journal of IEEE transactions on neural networks and learning systems_ , vol. 24, no. 4, pp. 666–672, 2013.
  * [94] L. Shi, Y. Zhang, J. Cheng, and H. Lu, “Skeleton-based action recognition with directed graph neural networks,” in _Proceedings of CVPR_ , 2019, pp. 7912–7921.
  * [95] C. Li, X. Qin, X. Xu, D. Yang, and G. Wei, “Scalable graph convolutional networks with fast localized spectral filter for directed graphs,” _Journal of IEEE Access_ , vol. 8, pp. 105 634–105 644, 2020.
  * [96] Y. Feng, H. You, Z. Zhang, R. Ji, and Y. Gao, “Hypergraph neural networks,” in _Proceedings of AAAI_ , ”2019”, pp. 3558–3565.
  * [97] B. Claude, _Hypergraphs: combinatorics of finite sets_. Elsevier, 1984, vol. 45.
  * [98] A. E. Caldwell, A. B. Kahng, and I. L. Markov, “Improved algorithms for hypergraph bipartitioning,” in _Proceedings of ASP-DAC_ , 2000, pp. 661–666.
  * [99] K. D. Devine, E. G. Boman, R. T. Heaphy, R. H. Bisseling, and U. V. Catalyurek, “Parallel hypergraph partitioning for scientific computing,” in _Proceedings of IPDPS_. IEEE, 2006, pp. 10–pp.
  * [100] L. Sun, S. Ji, and J. Ye, “Hypergraph spectral learning for multi-label classification,” in _Proceedings of KDD_ , ”2008”, pp. 668–676.
  * [101] S. Bulò and M. Pelillo, “A game-theoretic approach to hypergraph clustering,” _Journal of Advances in neural information processing systems_ , vol. 22, 2009.
  * [102] S. Bai, F. Zhang, and P. H. Torr, “Hypergraph convolution and hypergraph attention,” _Proceedings of Pattern Recognition_ , vol. 110, 2921.
  * [103] R. Zhang, Y. Zou, and J. Ma, “Hyper-sagnn: a self-attention based graph neural network for hypergraphs,” _Journal of arXiv preprint arXiv:1911.02613_ , 2019\.
  * [104] X. Sun, H. Yin, B. Liu, H. Chen, J. Cao, Y. Shao, and N. Q. V. Hung, “Heterogeneous hypergraph embedding for graph classification,” in _Proceedings of WSDM_ , 2021, pp. 725–733.
  * [105] Q. Fang, J. Sang, C. Xu, and Y. Rui, “Topic-sensitive influencer mining in interest-based social media networks via hypergraph learning,” _Journal of IEEE Transactions on Multimedia_ , vol. 16, no. 3, pp. 796–812, 2014.
  * [106] G. St-Onge, I. Iacopini, V. Latora, A. Barrat, G. Petri, A. Allard, and L. Hébert-Dufresne, “Influential groups for seeding and sustaining nonlinear contagion in heterogeneous hypergraphs,” _Journal of Communications Physics_ , vol. 5, no. 1, pp. 1–16, 2022.
  * [107] X. Yu, X. R. Y. Sun, Q. Gu, B. Sturt, U. Khandelwal, B. Norick, and J. Han, “Personalized entity recommendation: A heterogeneous information network approach,” in _Proceedings of WSDM_ , 2014, pp. 283–292.
  * [108] H. Gui, J. Liu, F. Tao, M. Jiang, B. Norick, L. Kaplan, and J. Han, “Embedding learning with events in heterogeneous information networks,” _IEEE transactions on knowledge and data engineering_ , vol. 29, no. 11, pp. 2428–2441, 2017.
  * [109] W.-L. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C.-J. Hsieh, “Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks,” in _Proceedings of SIGKDD_ , ”2019”, pp. 257–266.
  * [110] H. Chen, H. Yin, W. Wang, H. Wang, Q. V. H. Nguyen, and X. Li, “Pme: projected metric embedding on heterogeneous networks for link prediction,” in _Proceedings of ACM KDD_ , 2018, pp. 1177–1186.
  * [111] L. Xu, X. Wei, J. Cao, and P. S. Yu, “Embedding of embedding (eoe) joint embedding for coupled heterogeneous networks,” in _Proceedings of the tenth ACM international conference on web search and data mining_ , ”2017”, pp. 741–749.
  * [112] B. Hu, Y. Fang, and C. Shi, “Adversarial learning on heterogeneous information networks,” in _Proceedings of ACM KDD_ , 2019, pp. 120–129.
  * [113] L. Hu, T. Yang, C. Shi, H. Ji, and X. Li, “Heterogeneous graph attention networks for semi-supervised short text classification,” in _Proceedings of EMNLP_ , 2019, pp. 4821–4830.
  * [114] C. Shi, B. Hu, W. X. Zhao, and P. S. Yu, “Heterogeneous information network embedding for recommendation,” _IEEE Transactions on Knowledge and Data Engineering_ , vol. 31, no. 2, pp. 357–370, 2019.
  * [115] Z. Chen, X. Li, and J. Bruna, “Supervised community detection with line graph neural networks,” _arXiv preprint arXiv:1705.08415_ , 2017.
  * [116] J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun, “Spectral networks and locally connected networks on graphs,” in _Proceedings of ICLR_ , ”2013”.
  * [117] M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in _Proceedings of NIPS_ , ”2016”, pp. 3844––3852.
  * [118] B. Xu, H. Shen, Q. Cao, K. Cen, and X. Cheng, “Graph convolutional networks using heat kernel for semi-supervised learning,” _arXiv preprint arXiv:2007.16002_ , 2020.
  * [119] J. Atwood and D. Towsley, “Diffusion-convolutional neural networks,” in _Proceedings of NIPS_ , ”2016”, pp. 1993––2001.
  * [120] M. Niepert, M. Ahmed, and K. Kutzkov, “Learning convolutional neural networks for graphs,” in _Proceedings of ICML_ , ”2016”, pp. 2014–2023.
  * [121] K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” in _Proceedings of ICLR_ , ”2019”.
  * [122] Y. Seo, M. D. Vandergheynst, and X. Bresson, “Structured sequence modeling with graph convolutional recurrent networks,” in _Proceedings of_ , 2018, pp. 362–373.
  * [123] F. Manessi, R. ozza, and M. Manzo, “Dynamic graph convolutional networks,” _Journal of Pattern Recognition_ , vol. 97, p. 107000, 2020.
  * [124] A. Taheri, K. Gimpel, and T. Berger-Wolf, “Learning to represent the evolution of dynamic graphs with recurrent models,” in _Companion Proceedings of WWW_ , ”2019”, pp. 301–307.
  * [125] J. Ye, Z. Liu, B. Du, L. Sun, W. Li, Y. Fu, and H. Xiong, “Learning the evolutionary and multi-scale graph structure for multivariate time series forecasting,” in _Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_ , 2022, pp. 2296–2306.
  * [126] Z. Shao, Z. Zhang, F. Wang, and Y. Xu, “Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting,” in _Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_ , 2022, pp. 1567–1577.
  * [127] J. Chen, X. Wang, and X. Xu, “Gc-lstm: Graph convolution embedded lstm for dynamic link prediction,” _arXiv preprint arXiv:1812.04206_ , 2018.
  * [128] A. Pareja, G. Domeniconi, J. Chen, T. Ma, T. Suzumura, H. Kanezashi, T. Kaler, T. Schardl, and C. Leiserson, “Evolvegcn: Evolving graph convolutional networks for dynamic graphs,” in _Proceedings of the AAAI Conference on Artificial Intelligence_ , vol. 34, no. 04, 2020, pp. 5363–5370.
  * [129] P. Goyal, S. R. Chhetri, and A. Canedo, “dyngraph2vec: Capturing network dynamics using dynamic graph representation learning,” _Knowledge-Based Systems_ , vol. 187, p. 104816, 2020.
  * [130] Y. Ma, Z. Guo, Z. Ren, J. Tang, and D. Yin, “Streaming graph neural networks,” in _Proceedings of ACM SIGIR_ , ”2020”, pp. 719–728.
  * [131] S. Kumar, X. Zhang, and J. Leskovec, “Predicting dynamic embedding trajectory in temporal interaction networks,” in _Proceedings of SIGKDD_ , ”2019”, pp. 1269–1278.
  * [132] R. Trivedi, M. Farajtabar, P. Biswal, and H. Zha, “Dyrep: Learning representations over dynamic graphs,” in _International conference on learning representations_ , 2019.
  * [133] R. Trivedi, H. Dai, Y. Wang, and L. Song, “Know-evolve: Deep temporal reasoning for dynamic knowledge graphs,” in _Proceedings of ICML_. PMLR, ”2017”, pp. 3462–3471.
  * [134] S. Sajadmanesh, S. Bazargani, J. Zhang, and H. R. Rabiee, “Continuous-time relationship prediction in dynamic heterogeneous information networks,” _ACM Transactions on Knowledge Discovery from Data (TKDD)_ , vol. 13, no. 4, pp. 1–31, 2019.
  * [135] E. Rossi, B. Chamberlain, F. Frasca, D. Eynard, F. Monti, and M. Bronstein, “Temporal graph networks for deep learning on dynamic graphs,” _arXiv preprint arXiv:2006.10637_ , 2020.
  * [136] L. Han, X. Ma, L. Sun, B. Du, Y. Fu, W. Lv, and H. Xiong, “Continuous-time and multi-level graph representation learning for origin-destination demand prediction,” in _Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_ , 2022, pp. 516–524.
  * [137] M. Zhang, S. Wu, X. Yu, Q. Liu, and L. Wang, “Dynamic graph neural networks for sequential recommendation,” _IEEE Transactions on Knowledge and Data Engineering_ , 2022.
  * [138] Z. Zhang, H. Lin, Y. Gao, and K. BNRist, “Dynamic hypergraph structure learning.” in _Proceedings of IJCAI_ , ”2018”, pp. 3162–3169.
  * [139] J. Jiang, Y. Wei, Y. Feng, J. Cao, and Y. Gao, “Dynamic hypergraph neural networks.” in _Proceedings of IJCAI_ , ”2019”, pp. 2635–2641.
  * [140] X. Zhu, S. Zhang, Y. Zhu, P. Zhu, and Y. Gao, “Unsupervised spectral feature selection with dynamic hyper-graph learning,” _IEEE Transactions on Knowledge and Data Engineering_ , 2020.
  * [141] S. Liu, P. Lv, Y. Zhang, J. Fu, J. Cheng, W. Li, B. Zhou, and M. Xu, “Semi-dynamic hypergraph neural network for 3d pose estimation.” in _IJCAI_ , 2020, pp. 782–788.
  * [142] B. Wang, Y. Sun, Z. Sun, L. D. Nguyen, and T. Q. Duong, “Uav-assisted emergency communications in social iot: A dynamic hypergraph coloring approach,” _IEEE Internet of Things Journal_ , vol. 7, no. 8, pp. 7663–7677, 2020.
  * [143] J. Wang, Y. Zhang, Y. Wei, Y. Hu, X. Piao, and B. Yin, “Metro passenger flow prediction via dynamic hypergraph convolution networks,” _IEEE Transactions on Intelligent Transportation Systems_ , vol. 22, no. 12, pp. 7891–7903, 2021.
  * [144] G. Jeh and J. Widom, “Simrank: a measure of structural-context similarity,” in _Proceedings of KDD_ , ”2002”, pp. 538–543.
  * [145] D. Koutra, H. Tong, and D. Lubensky, “Big-align: Fast bipartite graph alignment,” in _Proceedings of IEEE_ , ”2013”, pp. 389–398.
  * [146] X. Jin, C. X. Lin, J. Luo, and J. Han, “Socialspamguard: A data mining-based spam detection system for social media networks,” _Journal of the VLDB Endowment_ , vol. 4, no. 12, pp. 1458–1461, 2011.
  * [147] J. Zhang and S. Y. Philip, “Multiple anonymized social networks alignment,” in _Proceedings of IEEE_ , ”2015”, pp. 599–608.
  * [148] D. Zhou, S. A. Orshanskiy, H. Zha, and C. L. Giles, “Co-ranking authors and documents in a heterogeneous network,” in _Proceedings of ICDM_ , ”2007”, pp. 739–744.
  * [149] L. Liu, J. Tang, J. Han, M. Jiang, and S. Yang, “Mining topic-level influence in heterogeneous networks,” in _Proceedings of ACM_ , ”2010”, pp. 199–208.
  * [150] C. Shi, C. Zhou, X. Kong, P. S. Yu, G. Liu, and B. Wang, “Heterecom: a semantic-based recommendation system in heterogeneous networks,” in _Proceedings of KDD_ , ”2012”, pp. 1552–1555.
  * [151] C. Luo, R. Guan, Z. Wang, and C. Lin, “Hetpathmine: A novel transductive classification algorithm on heterogeneous information networks,” in _Proceedings of ECIR_ , ”2014”, pp. 210–221.
  * [152] D. Zhang, J. Yin, X. Zhu, and C. Zhang, “User profile preserving social network embedding,” in _Proceedings of IJCAI_ , ”2017”, pp. 3378–3384.
  * [153] L. Xu, X. Wei, J. Cao, and P. S. Yu, “On exploring semantic meanings of links for embedding social networks,” in _Proceedings of the WWW_ , 2018, pp. 479–488.
  * [154] D. Marcheggiani and I. Titov, “Encoding sentences with graph convolutional networks for semantic role labeling,” in _Proceedings of EMNLP_ , ”2017”, pp. 1506–1515.
  * [155] T. Long, R. Lowe, J. C. K. Cheung, and D. Precup, “Leveraging lexical resources for learning entity embeddings in multi-relational data,” _Journal of arXiv preprint arXiv:1605.05416_ , 2016.
  * [156] S. Bourigault, C. Lagnier, S. Lamprier, L. Denoyer, and P. Gallinari, “Learning social network embeddings for predicting information diffusion,” in _Proceedings of WSDM_ , ”2014”, pp. 393–402.
  * [157] T. Man, H. Shen, S. Liu, X. Jin, and X. Cheng, “Predict anchor links across social networks via an embedding approach,” in _Proceedings of IJCAI_ , ”2016”, pp. 1823–1829.
  * [158] Y. Zhang, S. Gao, J. Pei, and H. Huang, “Improving social network embedding via new second-order continuous graph neural networks,” in _Proceedings of SIGKDD_. New York, NY, USA: Association for Computing Machinery, 2022, p. 2515–2523.
  * [159] T. M. Le and H. W. Lauw, “Probabilistic latent document network embedding,” in _Proceedings of ICDM_ , ”2014”, pp. 270–279.
  * [160] G. Cui, J. Zhou, C. Yang, and Z. Liu, “Adaptive graph encoder for attributed graph embedding,” in _Proceedings of SIGKDD_ , ”2020”, pp. 976–985.
  * [161] C. Park, D. Kim, J. Han, and H. Yu, “Unsupervised attributed multiplex network embedding,” in _Proceedings of AAAI_ , ”2020”, pp. 5371–5378.
  * [162] B. Rozemberczki, C. Allen, and R. Sarkar, “Multi-scale attributed node embedding,” _Journal of Complex Networks_ , vol. 9, no. 2, 05 2021.
  * [163] C. Aggarwal, G. He, and P. Zhao, “Edge classification in networks,” in _Proceedings of ICDE_ , 2016, pp. 1038–1049.
  * [164] H. Deng, J. Han, B. Zhao, Y. Yu, and C. X. Lin, “Probabilistic topic models with biased propagation on heterogeneous information networks,” in _Proceedings of KDD_ , ”2011”, pp. 1271–1279.
  * [165] L. Xu, X. Wei, J. Cao, and P. S. Yu, “Embedding of embedding (eoe): Joint embedding for coupled heterogeneous networks,” in _Proceedings of WSDM_. Association for Computing Machinery, 2017, p. 741–749.
  * [166] C. Shi, Z. Zhang, P. Luo, P. S. Yu, Y. Yue, and B. Wu, “Semantic path based personalized recommendation on weighted heterogeneous information networks,” in _Proceedings of the 24th ACM International on Conference on Information and Knowledge Management_ , 2015, pp. 453–462.
  * [167] A. Neelakantan, B. Roth, and A. McCallum, “Compositional vector space models for knowledge base completion,” _Journal of arXiv preprint arXiv:1504.06662_ , 2015.
  * [168] Y. Lin, Z. Liu, H. Luan, M. Sun, S. Rao, and S. Liu, “Modeling relation paths for representation learning of knowledge bases,” in _Proceedings of EMNLP_ , ”2015”, pp. 705–714.
  * [169] K. Toutanova, X. V. Lin, W. tau Yih, H. Poon, and C. Quirk, “Compositional learning of embeddings for relation paths in knowledge base and text,” in _Proceedings of AMACL_ , ”2016”, pp. 434–1444.
  * [170] R. Khasanova and P. Frossard, “Graph-based isometry invariant representation learning,” in _Proceedings of ICML_ , ”2017”, pp. 1847–1856.
  * [171] R. Ying, J. You, C. Morris, X. Ren, W. L. Hamilton, and J. Leskovec, “Hierarchical graph representation learning with differentiable pooling,” in _Proceedings of NIPS_ , ”2018”, pp. 4801––4811.
  * [172] R. Li, S. Wang, F. Zhu, and J. Huang, “Adaptive graph convolutional neural networks,” in _Proceedings of AAAI_ , ”2018”.
  * [173] S. Pan, R. Hu, G. Long, J. Jiang, L. Yao, and C. Zhang, “Adversarially regularized graph autoencoder for graph embedding,” in _Proceedings of IJCAI_ , ”2018”, pp. 2609–2615.
  * [174] Z. Guo, Y. Shen, A. K. Bashir, K. Yu, and J. C. wei Lin, “Graph embedding-based intelligent industrial decision for complex sewage treatment processes,” _INT J INTELL SYST_ , 2021.
  * [175] Y. Li, W. Ouyang, B. Zhou, J. Shi, C. Zhang, and X. Wang, “Factorizable net: an efficient subgraph-based framework for scene graph generation,” in _Proceedings of ECCV. Springer_ , 2018, p. 346–363.
  * [176] J. Yang, J. Lu, S. Lee, D. Batra, and D. Parikh, “Graph r-cnn for scene graph generation,” in _Proceedings of ECCV. Springer_ , 2018, pp. 690––706.
  * [177] M. E. Newman, “Detecting community structure in networks,” _Journal of The European physical journal B_ , vol. 38, no. 2, pp. 321–330, 2004.
  * [178] QianyiZhan, J. Zhang, S. Wang, P. S. Yu, and J. Xie, “Proceedings of influence maximization across partially aligned heterogenous social networks,” in _Pacific-Asia conference on knowledge discovery and data mining_ , ”2015”, pp. 58–69.
  * [179] S. Pan, J. Wu, X. Zhu, G. Long, and C. Zhang, “Task sensitive feature exploration and learning for multitask graph classification,” _Proceedings of IEEE Transactions on Neural Networks_ , vol. 47, no. 3, pp. 744––758, 2017.
  * [180] M. Zhang, Z. Cui, M. Neumann, and Y. Chen, “An end-to-end deep learning architecture for graph classification,” in _Proceedings of AAAI_ , ”2018”.
  * [181] S. F. Mousavi, M. Safayani, A. Mirzaei, and H. Bahonar, “Hierarchical graph embedding in vector space by graph pyramid,” _Proceedings of Pattern Recognition_ , vol. 61, pp. 245–254, 2017.
  * [182] Z. Peng, W. Huang, MinnanLuo, Q. Zheng, Y. Rong, T. Xu, , and J. Huang, “Graph representation learning via graphical mutual information maximization,” in _Proceedings of WWW_ , ”2020”, pp. 259–270.
  * [183] T. Rocktäschel, S. Singh, and S. Riedel, “Injecting logical background knowledge into embeddings for relation extraction,” in _Proceedings of NAACL_ , ”2015”, pp. 1119–1129.
  * [184] H. Liu, Y. Wu, and Y. Yang, “Analogical inference for multi-relational embeddings,” in _Proceedings of ICML_. PMLR, ”2017”, pp. 2168–2178.
  * [185] T. Trouillon, J. Welbl, S. Riedel, E. Gaussier, and G. Bouchard, “Complex embeddings for simple link prediction.” in _Proceedings of ICML_ , ”2016”, pp. 2071–2080.
  * [186] R. Das, S. Dhuliawala, M. Zaheer, L. Vilnis, I. Durugkar, A. Krishnamurthy, A. Smola, and A. McCallum, “Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning,” _Journal of arXiv preprint arXiv:1711.05851_ , 2017.
  * [187] S. Cavallari, V. W. Zheng, H. Cai, K. C.-C. Chang, and E. Cambria, “Learning community embedding with community detection and node embedding on graphs,” in _Proceedings of CIKM_ , ”2017”, pp. 377–386.
  * [188] H. Yao, F. Wu, J. Ke, X. Tang, Y. Jia, S. Lu, P. Gong, J. Ye, and Z. Li, “Deep multi-view spatial-temporal network for taxi demand prediction,” in _Proceedings of AAAI_ , 2018, p. 2588–2595.
  * [189] K. Hassani and A. H. Khasahmadi, “Contrastive multi-view representation learning on graphs,” in _Proceedings of ICML_ , ”2020”, pp. 4116––4126.
  * [190] M. Fey and J. E. Lenssen, “Fast graph representation learning with pytorch geometric,” _Journal of arXiv preprint arXiv:1903.02428_ , 2019.
  * [191] J. Cao, X. Lin, S. Guo, L. Liu, T. Liu, and B. Wang, “Bipartite graph embedding via mutual information maximization,” in _Proceedings of the WSDM_ , 2021, pp. 635–643.
  * [192] T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in _Proceedings of ICLR_ , ”2016”.
  * [193] H. Wang, J. Wang, J. Wang, M. Zhao, W. Zhang, F. Zhang, X. Xie, and M. Guo, “Graphgan: Graph representation learning with generative adversarial nets,” in _Proceedings of AAAI_ , vol. 32, no. 1, ”2018”.
  * [194] S. Cao, W. Lu, and Q. Xu, “Deep neural networks for learning graph representations,” in _Proceedings of AAAI_ , ”2016”, pp. 1145–1152.
  * [195] C. Mavromatis and G. Karypis, “Graph infoclust: Leveraging cluster-level node information for unsupervised graph representation learning,” _Journal of arXiv preprint arXiv:2009.06946_ , 2020.
  * [196] L. F. Ribeiro, P. H. Saverese, and D. R. Figueiredo, “Struc2vec: Learning node representations from structural identity,” in _Proceedings of KDD_ , ”2017”, pp. 385––394.
  * [197] X. Huang, J. Li, and X. Hu, “Label informed attributed network embedding,” in _Proceedings of WSDM_ , ”2017”, pp. 731–739.
  * [198] K. Sun, Z. Lin, and Z. Zhu, “Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes,” in _Proceedings of AAAI_ , ”2020”, pp. 5892–5899.
  * [199] Y. You, T. Chen, Z. Wang, and Y. Shen, “When does self-supervision help graph convolutional networks?” in _Proceedings of ICML_ , ”2020”, pp. 10 871–10 880.
  * [200] D. Zhou, J. Huang, and B. Schölkopf, “Learning with hypergraphs: Clustering, classification, and embedding,” _Journal of Advances in neural information processing systems_ , vol. 19, pp. 1601–1608, 2006.
  * [201] S. Fortunato, “Community detection in graphs,” _Journal of Physics reports_ , vol. 486, no. 3-5, pp. 75–174, 2010.
  * [202] S. Bhagat, G. Cormode, and S. Muthukrishnan, “Node classification in social networks,” in _Proceedings of SNDA_ , ”2011”, pp. 115–148.
  * [203] S. White and P. Smyth, “A spectral clustering approach to finding communities in graphs,” in _Proceedings of SIAM_. SIAM, ”2005”, pp. 274–285.
  * [204] J. Leskovec and J. Mcauley, “Learning to discover social circles in ego networks,” in _Proceedings of NIPS_ , vol. 25, 2012.
  * [205] C. H. Ding, X. He, H. Zha, M. Gu, and H. D. Simon, “A min-max cut algorithm for graph partitioning and data clustering,” in _Proceedings of IEEE_ , ”2001”, pp. 107–114.
  * [206] Z. Wang, C. Du, J. Fan, and Y. Xing, “Ranking influential nodes in social networks based on node position and neighborhood,” _Neurocomputing_ , vol. 260, pp. 466–477, 2017.
  * [207] S. Lee, S. Park, M. Kahng, and S.-g. Lee, “Pathrank: A novel node ranking measure on a heterogeneous graph for recommender systems,” in _Proceedings of the 21st ACM international conference on Information and knowledge management_ , 2012, pp. 1637–1641.
  * [208] A. C. Gilbert and K. Levchenko, “Compressing network graphs,” in _Proceedings of ACM_ , ”2004”.
  * [209] Y. Dong, J. Kang, H. Tong, and J. Li, “Individual fairness for graph neural networks: A ranking based approach,” in _Proceedings of the KDD_ , 2021, pp. 300–310.
  * [210] R. Singh, J. Xu, and B. Berger, “Pairwise global alignment of protein interaction networks by matching neighborhood topology,” in _Proceedings of RECOMB_ , ”2007”, pp. 16–31.
  * [211] H. Huang, A. Zubiaga, H. Ji, H. Deng, D. Wang, H. Le, T. Abdelzaher, J. Han, A. Leung, J. Hancock _et al._ , “Tweet ranking based on heterogeneous networks,” in _Proceedings of COLING 2012_ , ”2012”, pp. 1239–1256.
  * [212] Y. Li, C. Shi, S. Y. Philip, and Q. Chen, “How to draw a planar graph on a grid,” in _Proceedings of International Conference on Web-Age Information Management_ , 2014, pp. 553–565.
  * [213] J. McAuley, R. Pandey, and J. Leskovec, “Inferring networks of substitutable and complementary products,” in _Proceedings of KDD_ , ”2015”, pp. 785–794.
  * [214] M. Pirouz, “Optimized ranking-based community detection,” in _2021 International Conference on Computational Science and Computational Intelligence (CSCI)_. IEEE, 2021, pp. 1406–1409.
  * [215] Z. Tianming, G. Yunjun, Z. Baihua, C. Lu, W. Shiting, and G. Wei, “Towards distributed node similarity search on graphs,” _World Wide Web_ , vol. 23, no. 6, pp. 3025–3053, 2020.
  * [216] C. Shi, X. Kong, Y. Huang, S. Y. Philip, and B. Wu, “Hetesim: A general framework for relevance measure in heterogeneous networks,” _Proceedings of IEEE_ , vol. 26, no. 10, pp. 2479–2492, 2014.
  * [217] Y. Xiong, Y. Zhu, and S. Y. Philip, “Top-k similarity join in heterogeneous information networks,” _Journal of IEEE Transactions on Knowledge and Data Engineering_ , vol. 27, no. 6, pp. 1710–1723, 2014.
  * [218] N. Natarajan and I. S. Dhillon, “Inductive matrix completion for predicting gene–disease associations,” _Journal of Bioinformatics_ , vol. 30, no. 12, pp. i60–i68, 2014.
  * [219] W. Litian, H. Wenhui, Z. Tong, Y. Shanshan, Z. Xiaoya, and L. Xueyang, “Node similarity-based search method for medical insurance heterogeneous information network,” in _2022 IEEE 8th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing,(HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS)_. IEEE, 2022, pp. 174–179.
  * [220] B. Cao, X. Kong, and S. Y. Philip, “Collective prediction of multiple types of links in heterogeneous information networks,” in _Proceedings of IEEE_. IEEE, ”2014”, pp. 50–59.
  * [221] W. Jin, T. Derr, Y. Wang, Y. Ma, Z. Liu, and J. Tang, “Node similarity preserving graph convolutional networks,” in _Proceedings of the 14th ACM international conference on web search and data mining_ , 2021, pp. 148–156.
  * [222] A. Saxena, G. Fletcher, and M. Pechenizkiy, “Nodesim: node similarity based network embedding for diverse link prediction,” _EPJ Data Science_ , vol. 11, no. 1, p. 24, 2022.
  * [223] D. Liben-Nowell and J. Kleinberg, “The link prediction problem for social networks,” in _Proceedings of CIKM_ , ”2003”, pp. 556–559.
  * [224] K. Abbas, A. Abbasi, S. Dong, L. Niu, L. Yu, B. Chen, S.-M. Cai, and Q. Hasan, “Application of network link prediction in drug discovery,” _BMC bioinformatics_ , vol. 22, no. 1, pp. 1–21, 2021.
  * [225] A. Clauset, C. Moore, and M. E. Newman, “Hierarchical structure and the prediction of missing links in networks,” _Journal of Nature_ , vol. 453, no. 7191, pp. 98–101, 2008.
  * [226] R. N. Lichtenwalter, J. T. Lussier, and N. V. Chawla, “New perspectives and methods in link prediction,” in _Proceedings of the KDD_ , 2010, pp. 243–252.
  * [227] Z. Zhu, Z. Zhang, L.-P. Xhonneux, and J. Tang, “Neural bellman-ford networks: A general graph neural network framework for link prediction,” _Advances in Neural Information Processing Systems_ , vol. 34, pp. 29 476–29 490, 2021.
  * [228] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” _neural information processing systems_ , 2013.
  * [229] K. Berahmand, E. Nasiri, M. Rostami, and S. Forouzandeh, “A modified deepwalk method for link prediction in attributed social network,” _Computing_ , vol. 103, no. 10, pp. 2227–2249, 2021.
  * [230] A. Kumari, R. K. Behera, K. S. Sahoo, A. Nayyar, A. Kumar Luhach, and S. Prakash Sahoo, “Supervised link prediction using structured-based feature extraction in social network,” _Concurrency and Computation: practice and Experience_ , vol. 34, no. 13, p. e5839, 2022.
  * [231] W. Chen, Y. Wang, and S. Yang, “Efficient influence maximization in social networks,” in _Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining_ , 2009, p. 199–208.
  * [232] E. Cohen, D. Delling, T. Pajor, and R. F. Werneck, “Sketch-based influence maximization and computation: Scaling up with guarantees,” _conference on information and knowledge management_ , 2014.
  * [233] C. Wang, W. Chen, and Y. Wang, “Scalable influence maximization for independent cascade model in large-scale social networks,” _Data Mining and Knowledge Discovery_ , 2012.
  * [234] W. Chen, Y. Yuan, and L. Zhang, “Scalable influence maximization in social networks under the linear threshold model,” in _ICDM 2010, The 10th IEEE International Conference on Data Mining, Sydney, Australia, 14-17 December 2010_ , ”2011”.
  * [235] X. He, G. Song, W. Chen, and Q. Jiang, “Influence blocking maximization in social networks under the competitive linear threshold model,” _siam international conference on data mining_ , 2012.
  * [236] A. Bozorgi, S. Samet, J. Kwisthout, and T. Wareham, “Community-based influence maximization in social networks under a competitive linear threshold model,” _Knowledge Based Systems_ , 2017.
  * [237] M. Xie, Q. Yang, Q. Wang, G. Cong, and G. de Melo, “Dynadiffuse: a dynamic diffusion model for continuous time constrained influence maximization,” _national conference on artificial intelligence_ , 2015.
  * [238] W. Li, K. Zhong, J. Wang, and D. Chen, “A dynamic algorithm based on cohesive entropy for influence maximization in social networks,” _Expert Systems With Applications_ , 2021.
  * [239] H. T. Nguyen, M. T. Thai, and T. N. Dinh, “Stop-and-stare: Optimal sampling algorithms for viral marketing in billion-scale networks,” in _the 2016 International Conference_ , 2016.
  * [240] M. Newman and M. Girvan, “Finding and evaluating community structure in networks.” _Physical Review E_ , 2004.
  * [241] D. Jin, Z. Liu, W. Li, D. He, and W. Zhang, “Graph convolutional networks meet markov random fields: Semi-supervised community detection in attribute networks,” _national conference on artificial intelligence_ , 2019.
  * [242] Z. Chen, L. Li, and J. Bruna, “Supervised community detection with line graph neural networks,” _Learning_ , 2019.
  * [243] S. Gregory, “An algorithm to find overlapping community structure in networks,” _european conference on machine learning_ , 2007.
  * [244] O. Shchur and S. Günnemann, “Overlapping community detection with graph neural networks,” _arXiv: Learning_ , 2018.
  * [245] Y. Jia, Q. Zhang, W. Zhang, and X. Wang, “Communitygan: Community detection with generative adversarial nets,” _the web conference_ , 2019.
  * [246] C. Tantipathananandh, T. Y. Berger-Wolf, and D. Kempe, “A framework for community identification in dynamic social networks,” _knowledge discovery and data mining_ , 2007.
  * [247] A. Anagnostopoulos, J. Łącki, S. Lattanzi, S. Leonardi, and M. Mahdian, “Community detection on evolving graphs,” _neural information processing systems_ , 2016.
  * [248] J. Leskovec and J. McAuley, “Learning to discover social circles in ego networks,” _neural information processing systems_ , 2012.
  * [249] S. Shirazi, A. Albadvi, E. Akhondzadeh, F. Farzadfar, and B. Teimourpour, “A new application of community detection for identifying the real specialty of physicians.” _International Journal of Medical Informatics_ , 2020.
  * [250] R. Ying, J. You, C. Morris, X. Ren, W. L. Hamilton, and J. Leskovec, “Hierarchical graph representation learning with differentiable pooling,” _neural information processing systems_ , 2018.
  * [251] W. L. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” _neural information processing systems_ , 2017.
  * [252] M. Zhang, Z. Cui, M. Neumann, and Y. Chen, “An end-to-end deep learning architecture for graph classification,” _national conference on artificial intelligence_ , 2018.
  * [253] H. Peng, J. Li, Q. Gong, Y. Ning, S. Wang, and L. He, “Motif-matching based subgraph-level attentional convolutional network for graph classification,” _national conference on artificial intelligence_ , 2020.
  * [254] M. Simonovsky and N. Komodakis, “Dynamic edge-conditioned filters in convolutional neural networks on graphs,” in _2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_ , 2017, pp. 29–38.
  * [255] J. Zeng and P. Xie, “Contrastive self-supervised learning for graph classification,” _national conference on artificial intelligence_ , 2020\.
  * [256] C. Gallicchio and A. Micheli, “Fast and deep graph neural networks,” _national conference on artificial intelligence_ , 2019.
  * [257] J. B. Lee, R. A. Rossi, and X. Kong, “Graph classification using structural attention,” _knowledge discovery and data mining_ , 2018.
  * [258] Q. Sun, H. Peng, J. Li, J. Wu, Y. Ning, P. S. Yu, and L. He, “Sugar: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism,” _Proceedings of the Web Conference 2021_ , 2021.
  * [259] S. Martin and K. Nikos, “Dynamic edge-conditioned filters in convolutional neural networks on graphs,” _IEEE Conference Proceedings_ , 2017.
  * [260] A. Fout, J. Byrd, B. Shariat, and A. Ben-Hur, “Protein interface prediction using graph convolutional networks,” _neural information processing systems_ , 2017.
  * [261] Z. Xiong, D. Wang, X. Liu, F. Zhong, X. Wan, X. Li, Z. Li, X. Luo, K. Chen, H. Jiang, and M. Zheng, “Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism.” _Journal of Medicinal Chemistry_ , 2020.
  * [262] A. Ahmed, N. Shervashidze, S. Narayanamurthy, V. Josifovski, and A. J. Smola, “Distributed large-scale natural graph factorization,” in _Proceedings of WWW_ , ”2013”, pp. 37–48.
  * [263] M. Ou, P. C. J. Pei, Z. Zhang, and Wenwu, “Asymmetric transitivity preserving graph embedding,” in _Proceedings of KDD_ , ”2016”, pp. 1105–1114.
  * [264] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” _Journal of arXiv preprint arXiv:1301.3781_ , 2013.
  * [265] B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in _Proceedings of KDD_ , ”2014”, pp. 701–710.
  * [266] N. Lao and W. W. Cohen, “Relational retrieval using a combination of path-constrained random walks,” _Journal of Machine learning_ , vol. 81, no. 1, pp. 53–67, 2010.
  * [267] C. Zhou, Y. Liu, X. Liu, Z. Liu, and J. Gao, “Scalable graph embedding forasymmetric proximity.” in _Proceedings of AAAI Conference on Artificial Intelligence_ , 2017.
  * [268] Y. Zhou, C. Wu, and L. Tan, “Biased random walk with restart for link prediction with graph embedding method,” _Physica A: Statistical Mechanics and Its Applications_ , vol. 570, p. 125783, 2021.
  * [269] C. Yang, Z. Liu, D. Zhao, M. Sun, and E. Y. Chang, “Network representation learning with rich text information,” in _Proceedings of IJCAI_ , ”2015”, pp. 2111–2117.
  * [270] D. Wang, P. Cui, and W. Zhu, “Structural deep network embedding,” in _Proceedings of KDD_ , ”2016”, pp. 1225–1234.
  * [271] P. Velickovic, W. Fedus, W. L. Hamilton, P. Li, Y. Bengio, and R. D. Hjelm, “Deep graph infomax,” in _Proceedings of ICLR_ , ”2019”.
  * [272] K. Tu, P. Cui, X. Wang, P. S. Yu, and W. Zhu, “Deep recursive network embedding with regular equivalence,” in _Proceedings of KDD_ , ”2018”, pp. 2357–2366.
  * [273] M. Simonovsky and authorNikos Komodakis, “Graphvae: Towards generation of small graphs using variational autoencoders,” in _Proceedings of ICANN. Springer_ , ”2018”, pp. 412––422.
  * [274] T. Ma, J. Chen, and C. Xiao, “Constrained generation of semantically valid graphs via regularizing variational autoencoders,” in _Proceedings of NIPS_ , ”2018”, pp. 7113––7124.
  * [275] N. D. Cao and T. Kipf, “Molgan: An implicit generative model for small molecular graphs,” _Journal of arXiv preprint arXiv:1805.11973_ , 2018.
  * [276] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved training of wasserstein gans,” in _Proceedings of NIPS_ , ”2017”, pp. 5767––5777.
  * [277] W. Feng, J. Zhang, Y. Dong, Y. Han, H. Luan, Q. Xu, Q. Yang, E. Kharlamov, and J. Tang, “Graph random neural networks for semi-supervised learning on graphs,” _Advances in neural information processing systems_ , vol. 33, pp. 22 092–22 103, 2020.
  * [278] M. A. Hasan, V. Chaoji, S. Salem, and M. Zaki, “Link prediction using supervised learning,” in _SDM06: workshop on link analysis, counter-terrorism and security_ , vol. 30, 2006, pp. 798–805.
  * [279] F. Errica, M. Podda, D. Bacciu, and A. Micheli, “A fair comparison of graph neural networks for graph classification,” in _Proceedings of ICLR_ , ”2019”.
  * [280] L. Zhao, X. Peng, Y. Tian, M. Kapadia, and D. N. Metaxas, “Semantic graph convolutional networks for 3d human pose regression,” in _Proceedings of the IEEE/CVF conference on computer vision and pattern recognition_ , 2019, pp. 3425–3435.
  * [281] D. Zhou, J. Huang, and B. Scholkopf, “Learning from labeled and unlabeled data on a directed graph,” in _Proceedings of the 22nd international conference on Machine learning_ , 2005, pp. 1036–1043.
  * [282] F. Hu, Y. Zhu, S. Wu, L. Wang, and T. Tan, “Semi-supervised node classification via hierarchical graph convolutional networks,” _arXiv preprint arXiv:1902.06667_ , 2019.
  * [283] A. Rahimi, T. Cohn, and T. Baldwin, “Semi-supervised user geolocation via graph convolutional networks,” in _Proceedings of ACL_ , ”2018”, pp. 2009––2019.
  * [284] S. Wan, S. Pan, J. Yang, and C. Gong, “Contrastive and generative graph convolutional networks for graph-based semi-supervised learning,” in _Proceedings of AAAI_ , ”2021”.
  * [285] B. Jiang, Z. Zhang, D. Lin, J. Tang, and B. Luo, “Semi-supervised learning with graph learning-convolutional networks,” in _Proceedings of CVPR_ , 2019, pp. 11 313–11 320.
  * [286] N. Lee, J. Lee, and C. Park, “Augmentation-free self-supervised learning on graphs,” in _Proceedings of AAAI_ , vol. 36, no. 7, 2022, pp. 7372–7380.
  * [287] Y. Wang and J. M. Solomon, “Prnet: Self-supervised learning for partial-to-partial registration,” in _Proceedings of NIPS_ , vol. 32, 2019\.
  * [288] Z. Hu, Y. Dong, K. Wang, K.-W. Chang, and Y. Sun, “Gpt-gnn: Generative pre-training of graph neural networks,” in _Proceedings of SIGKDD_ , ”2020”, pp. 1857–1867.
  * [289] G. Kahn, A. Villaflor, B. Ding, P. Abbeel, and S. Levine, “Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation,” in _Proceedings of ICRA_. IEEE, 2018, pp. 5129–5136.
  * [290] J.-B. Alayrac, A. Recasens, R. Schneider, R. Arandjelović, J. Ramapuram, J. D. Fauw, L. Smaira, S. Dieleman, and A. Zisserman, “Self-supervised multimodal versatile networks,” vol. 33, pp. 25–37, 2020.
  * [291] X. Wang, N. Liu, H. Han, and C. Shi, “Self-supervised heterogeneous graph neural network with co-contrastive learning,” in _Proceedings of SIGKDD_ , 2021, pp. 1726–1736.
  * [292] Y. Ding, J. Zhuang, P. Ding, and M. Jia, “Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings,” _Journal of Reliability Engineering & System Safety_, vol. 218, p. 108126, 2022.
  * [293] P. Veliˇckovi´c, W. Fedus, W. L. Hamilton, P. Li‘o, Y. Bengio, and R. D. Hjelm, “Deep graph infomax,” _Journal of arXiv preprint arXiv:1809.10341_ , 2018.
  * [294] A. Goodge, B. Hooi, S.-K. Ng, and W. S. Ng, “Lunar: Unifying local outlier detection methods via graph neural networks,” in _Proceedings of AAAI_ , vol. 36, no. 6, 2022, pp. 6737–6745.
  * [295]
  * [296] R. Socher, D. Chen, C. D. Manning, and A. Y. Ng, “Reasoning with neural tensor networks for knowledge base completion,” _neural information processing systems_ , 2013.
  * [297] Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang, “Graph contrastive learning with adaptive augmentation,” in _Proceedings of WWW_ , ”2021”.
  * [298] S. Lin, C. Liu, P. Zhou, Z.-Y. Hu, S. Wang, R. Zhao, Y. Zheng, L. Lin, E. Xing, and X. Liang, “Prototypical graph contrastive learning,” _Journal of IEEE Transactions on Neural Networks and Learning Systems_ , 2022.
  * [299] J. Park, M. Lee, H. J. Chang, K. Lee, and J. Y. Choi, “Symmetric graph convolutional autoencoder for unsupervised graph representation learning,” in _Proceedings of ICCV_ , ”2019”, pp. 6519–6528.
  * [300] Y. Zhou, H. Cheng, and J. X. Yu, “Graph clustering based on structural/attribute similarities,” _Journal of VLDB Endowment_ , vol. 2, no. 1, pp. 718–729, 2009.
  * [301] T. Kipf, E. Fetaya, K.-C. Wang, M. Welling, and R. Zemel, “Neural relational inference for interacting systems,” in _Proceedings of ICML_ , ”2018”, pp. 2688–2697.
  * [302] D. Zhu, P. Cui, D. Wang, and W. Zhu, “Deep variational network embedding in wasserstein space,” in _Proceedings of ACM KDD_ , ”2018”, pp. 2827–2836.
  * [303] J. Tang and H. Liu, “Unsupervised feature selection for linked social media data,” in _Proceedings of KDD_ , ”2012”, pp. 904–912.
  * [304] M. Girvan and M. E. Newman, “Community structure in social and biological networks,” _Journal of the national academy of sciences_ , vol. 99, no. 12, pp. 7821–7826, 2002.
  * [305] X. Wan, X. Wu, D. Wang, X. Tan, X. Liu, Z. Fu, H. Jiang, M. Zheng, and X. Li, “An inductive graph neural network model for compound–protein interaction prediction based on a homogeneous graph,” _Journal of Briefings in Bioinformatics_ , vol. 23, no. 3, p. bbac073, 2022.
  * [306] Z. Wen and Y. Fang, “Trend: Temporal event and node dynamics for graph representation learning,” in _Proceedings of ACM_ , 2022, pp. 1159–1169.
  * [307] W. Huang, T. Zhang, Y. Rong, and J. Huang, “Adaptive sampling towards fast graph representation learning,” in _Proceedings of f NIPS_ , ”2018”, p. 4563–4572.
  * [308] B. Rath, A. Salecha, and J. Srivastava, “Detecting fake news spreaders in social networks using inductive representation learning,” in _Proceedings of ASONAM_. IEEE, 2020, pp. 182–189.
  * [309] I. Chami, R. Ying, C. Ré, and J. Leskovec, “Hyperbolic graph convolutional neural networks,” _Proceedings of Advances in neural information processing systems_ , vol. 32, no. 4869, 2019.
  * [310] K. Teru, E. Denis, and W. Hamilton, “Inductive relation prediction by subgraph reasoning,” in _Proceedings of ICML_. PMLR, 2020, pp. 9448–9457.
  * [311] Y. Wu, D. Zhuang, A. Labbe, and L. Sun, “Inductive graph neural networks for spatiotemporal kriging,” in _Proceedings of the AAAI_ , vol. 35, no. 5, 2021, pp. 4478–4485.
  * [312] Q. Lin, J. Liu, F. Xu, Y. Pan, Y. Zhu, L. Zhang, and T. Zhao, “Incorporating context graph with logical reasoning for inductive relation prediction,” in _Proceedings of ACM SIGIR_ , 2022, pp. 893–903.
  * [313] P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” in _Proceddings of ICLR_ , ”2018”.
  * [314] H. Gao and S. Ji, “Graph u-nets,” in _Proceedings of ICML_ , ”2019”, pp. 2083–2092.
  * [315] H. Gao, Z. Wang, and S. Ji, “Large-scale learnable graph convolutional networks,” in _Proceedings of KDD_ , ”2018”, pp. 1416–1424.
  * [316] Z. Liu, C. Chen, L. Li, J. Zhou, X. Li, L. Song, and Y. Qi, “Geniepath: Graph neural networks with adaptive receptive paths,” in _Proceedings of AAAI_ , 2019.
  * [317] Y. Chen, L. Wu, and M. Zaki, “Iterative deep graph learning for graph neural networks: Better and robust node embeddings,” _Journal of Advances in neural information processing systems_ , vol. 33, pp. 19 314–19 326, 2020.
  * [318] D. Kim and A. Oh, “How to find your friendly neighborhood: Graph attention design with self-supervision,” in _Proceedings of ICLR_ , ”2020”.
  * [319] A. Kazi, L. Cosmo, S.-A. Ahmadi, N. Navab, and M. Bronstein, “Differentiable graph module (dgm) for graph convolutional networks,” _Journal of IEEE Transactions on Pattern Analysis and Machine Intelligence_ , pp. 1–1, 2022.
  * [320] A.-L. Barabási and R. Albert, “Emergence of scaling in random networks,” _Journal of science_ , vol. 286, no. 5439, pp. 509–512, 1999.
  * [321] M. E. Newman and M. Girvan, “Finding and evaluating community structure in networks,” _Journal of Physical review E_ , vol. 69, no. 2, 2004.
  * [322] M. E. Newman, “Finding community structure in networks using the eigenvectors of matrices,” _Journal of Physical review E_ , vol. 74, no. 3, p. 036104, 2006.
  * [323] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” _Journal of Advances in neural information processing systems_ , vol. 26, 2013.
  * [324] J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “Line: Large-scale information network embedding,” in _Proceedings of WWW_ , ”2015”, pp. 1067–1077.
  * [325] S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin, “Graph embedding and extensions: A general framework for dimensionality reduction,” _Journal of IEEE transactions on pattern analysis and machine intelligence_ , vol. 29, no. 1, pp. 40–51, 2006.
  * [326] J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in _Proceedings of ICML_ , ”2017”, pp. 1263–1272.
  * [327] L. A. Adamic and E. Adar, “Friends and neighbors on the web,” _Journal of Social networks_ , vol. 25, no. 3, pp. 211–230, 2003.
  * [328] D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, “The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains,” _Proceedings of IEEE Signal Processing Magazine_ , vol. 30, no. 3, pp. 83––98, 2013.
  * [329] Z. Wang, J. Zhang, J. Feng, and Z. Chen, “Knowledge graph and text jointly embedding,” in _Proceedings of EMNLP_ , ”2014”, pp. 1591–1601.
  * [330] W. Fan, Y. Ma, Q. Li, Y. He, E. Zhao, J. Tang, and D. Yin, “Graph neural networks for social recommendation,” in _Proceedings of WWW_ , 2019, pp. 417–426.
  * [331] Y. Dong, J. Tang, S. Wu, J. Tian, N. V. Chawla, J. Rao, and H. Cao, “Link prediction and recommendation across heterogeneous social networks,” in _Proceedings of IEEE_ , ”2012”, pp. 181–190.
  * [332] J. Yu, H. Yin, J. Li, Q. Wang, N. Q. V. Hung, and X. Zhang, “Self-supervised multi-channel hypergraph convolutional network for social recommendation,” in _Proceedings of the Web Conference 2021_ , 2021, pp. 413–424.
  * [333] Y. Yang, C. Huang, L. Xia, Y. Liang, Y. Yu, and C. Li, “Multi-behavior hypergraph-enhanced transformer for sequential recommendation,” in _Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_ , 2022, pp. 2263–2274.
  * [334] L. Xia, C. Huang, and C. Zhang, “Self-supervised hypergraph transformer for recommender systems,” in _Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_ , 2022, pp. 2100–2109.
  * [335] W. Song, Z. Xiao, Y. Wang, L. Charlin, M. Zhang, and J. Tang, “Session-based social recommendation via dynamic graph attention networks,” in _Proceedings of ICDM_ , 2019, pp. 555–563.
  * [336] R. Ye, Y. Hou, T. Lei, Y. Zhang, Q. Zhang, J. Guo, H. Wu, and H. Luo, “Dynamic graph construction for improving diversity of recommendation,” in _Fifteenth ACM Conference on Recommender Systems_ , 2021, pp. 651–655.
  * [337] H. Liu, C. Zheng, D. Li, Z. Zhang, K. Lin, X. Shen, N. N. Xiong, and J. Wang, “Multi-perspective social recommendation method with graph representation learning,” _Neurocomputing_ , vol. 468, pp. 469–481, 2022.
  * [338] J. Yu, H. Yin, X. Xia, T. Chen, L. Cui, and Q. V. H. Nguyen, “Are graph augmentations necessary? simple graph contrastive learning for recommendation,” in _Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval_ , 2022, pp. 1294–1303.
  * [339] H. Peng, J. Li, Y. He, Y. Liu, M. Bao, L. Wang, Y. Song, and Q. Yang, “Large-scale hierarchical text classification with recursively regularized deep graph-cnn,” in _Proceedings of WWW_ , ”2018”, pp. 1063––1072.
  * [340] L. Yao, C. Mao, and Y. Luo, “Graph convolutional networks for text classification,” ”2019”, pp. 7370–7377.
  * [341] Y. Lin, Y. Meng, X. Sun, Q. Han, K. Kuang, J. Li, and F. Wu, “Bertgcn: Transductive text classification by combining gcn and bert,” _arXiv preprint arXiv:2105.05727_ , 2021.
  * [342] L. Zhang, J. Ding, Y. Xu, Y. Liu, and S. Zhou, “Weakly-supervised text classification based on keyword graph,” _arXiv preprint arXiv:2110.02591_ , 2021.
  * [343] Y. Zhao, L. Wang, C. Wang, H. Du, S. Wei, H. Feng, Z. Yu, and Q. Li, “Multi-granularity heterogeneous graph attention networks for extractive document summarization,” _Neural Networks_ , vol. 155, pp. 340–347, 2022\.
  * [344] L. Song, Y. Zhang, Z. Wang, and D. Gildea, “N-ary relation extraction using graph state lstm,” in _Proceedings of arXiv preprint arXiv:1808.09101_ , ”2018”.
  * [345] Y. Zhang, P. Qi, and C. D. Manning, “Graph convolution over runed dependency trees improves relation extraction,” in _Proceedings of arXiv preprint arXiv:1809.10185_ , ”2018”.
  * [346] X. Xu, T. Gao, Y. Wang, and X. Xuan, “Event temporal relation extraction with attention mechanism and graph neural network,” _Tsinghua Science and Technology_ , vol. 27, no. 1, pp. 79–90, 2021.
  * [347] Y. Zhao, H. Feng, H. Zhou, Y. Yang, X. Chen, R. Xie, F. Zhuang, and Q. Li, “Eigat: Incorporating global information in local attention for knowledge representation learning,” _Knowledge-Based Systems_ , vol. 237, p. 107909, 2022.
  * [348] Y. Zhao, S. Wei, H. Du, X. Chen, Q. Li, F. Zhuang, J. Liu, and G. Kou, “Learning bi-typed multi-relational heterogeneous graph via dual hierarchical attention networks,” _IEEE Transactions on Knowledge & Data Engineering_, no. 01, pp. 1–12, 2022.
  * [349] Y. Zhao, A. Zhang, R. Xie, K. Liu, and X. Wang, “Connecting embeddings for knowledge graph entity typing,” in _Proceedings of ACL_ , 2020, pp. 6419–6428.
  * [350] Y. Zhao, H. Zhou, A. Zhang, R. Xie, Q. Li, and F. Zhuang, “Connecting embeddings based on multiplex relational graph attention networks for knowledge graph entity typing,” _IEEE Transactions on Knowledge and Data Engineering_ , 2022.
  * [351] Z. Wang, J. Zhang, J. Feng, and Z. Chen, “Knowledge graph embedding by translating on hyperplanes.” in _Proceedings of AAAI_ , 2014, pp. 1112–1119.
  * [352] S. Liu, B. Grau, I. Horrocks, and E. Kostylev, “Indigo: Gnn-based inductive knowledge graph completion using pair-wise encoding,” _Advances in Neural Information Processing Systems_ , vol. 34, pp. 2034–2045, 2021.
  * [353] X. Zhang, A. Bosselut, M. Yasunaga, H. Ren, P. Liang, C. D. Manning, and J. Leskovec, “Greaselm: Graph reasoning enhanced language models for question answering,” _arXiv preprint arXiv:2201.08860_ , 2022.
  * [354] D. Teney, L. Liu, and A. van den Hengel, “Graph-structured representations for visual question answering,” in _Proceedings of CVPR_ , ”2017”, pp. 3233––3241.
  * [355] M. Narasimhan, S. Lazebnik, and A. G. Schwing, “Out of the box: Reasoning with graph convolution nets for factual visual question answering,” in _Proceedings of NeurIPS_ , ”2018”, pp. 2655––2666.
  * [356] J. Johnson, A. Gupta, and L. Fei-Fei, “Image generation from scene graphs,” in _Proceedings of IEEE_ , ”2018”, pp. 1219–1228.
  * [357] H. Zhang, X. Shang, H. Luan, M. Wang, and T.-S. Chua, “Learning from collective intelligence: Feature learning using social images and tags,” _Journal of ACM transactions on multimedia computing, communications, and applications_ , vol. 13, no. 1, pp. 1:1–1:23, 2016.
  * [358] M. Tang, F. Nie, and R. Jain, “Capped lp-norm graph embedding for photo clustering,” in _Proceedings of MM_ , ”2016”, pp. 431–435.
  * [359] E. E. Kosasih and A. Brintrup, “A machine learning approach for predicting hidden links in supply chain with graph neural networks,” _International Journal of Production Research_ , pp. 1–14, 2021.
  * [360] R. Cheng and Q. Li, “Modeling the momentum spillover effect for stock prediction via attribute-driven graph attention networks,” in _Proceedings of the AAAI Conference on Artificial Intelligence_ , vol. 35, no. 1, 2021, pp. 55–62.
  * [361] D. Cheng, Z. Niu, and Y. Zhang, “Contagious chain risk rating for networked-guarantee loans,” in _Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining_, 2020, pp. 2715–2723.
  * [362] D. Cheng, Y. Tu, Z.-W. Ma, Z. Niu, and L. Zhang, “Risk assessment for networked-guarantee loans using high-order graph attention representation.” in _IJCAI_ , 2019, pp. 5822–5828.
  * [363] J. Tan, Q. Li, J. Wang, and J. Chen, “Finhgnn: A conditional heterogeneous graph learning to address relational attributes for stock predictions,” _Information Sciences_ , 2022.
  * [364] J. Huang, R. Xing, and Q. Li, “Asset pricing via deep graph learning to incorporate heterogeneous predictors,” _International Journal of Intelligent Systems_ , vol. 37, no. 11, pp. 8462–8489, 2022.
  * [365] R. Cheng and Q. Li, “Subsequence-based graph routing network for capturing multiple risk propagation processes,” in _Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI)_ , 2022, pp. 3810–3816.
  * [366] W. Li, R. Bao, K. Harimoto, D. Chen, J. Xu, and Q. Su, “Modeling the stock relation with graph network for overnight stock movement prediction,” in _Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence_ , 2021, pp. 4541–4547.
  * [367] Y. Zheng, V. Lee, Z. Wu, and S. Pan, “Heterogeneous graph attention network for small and medium-sized enterprises bankruptcy prediction,” in _Pacific-Asia Conference on Knowledge Discovery and Data Mining_. Springer, 2021, pp. 140–151.
  * [368] Y. Zhao, S. Wei, Y. Guo, Q. Yang, and G. Kou, “Fisrebp: Enterprise bankruptcy prediction via fusing its intra-risk and spillover-risk,” _arXiv preprint arXiv:2202.03874_ , 2022.
  * [369] D. Wang, J. Lin, P. Cui, Q. Jia, Z. Wang, Y. Fang, Q. Yu, J. Zhou, S. Yang, and Y. Qi, “A semi-supervised graph attentive network for financial fraud detection,” in _2019 IEEE International Conference on Data Mining (ICDM)_. IEEE, 2019, pp. 598–607.
  * [370] D. Cheng, X. Wang, Y. Zhang, and L. Zhang, “Graph neural network for fraud detection via spatial-temporal attention,” _IEEE Transactions on Knowledge and Data Engineering_ , 2020.
  * [371] Y. Liu, X. Ao, Z. Qin, J. Chi, J. Feng, H. Yang, and Q. He, “Pick and choose: a gnn-based imbalanced learning approach for fraud detection,” in _Proceedings of the Web Conference 2021_ , 2021, pp. 3168–3177.
  * [372] Y. Ji, Z. Zhang, X. Tang, J. Shen, X. Zhang, and G. Yang, “Detecting cash-out users via dense subgraphs,” in _Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_ , 2022, pp. 687–697.
  * [373] T. Chen and C. Tsourakakis, “Antibenford subgraphs: Unsupervised anomaly detection in financial networks,” in _Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_ , 2022, pp. 2762–2770.
  * [374] C.-S. Liao, K. Lu, M. Baym, R. Singh, and B. Berger, “Isorankn: spectral methods for global alignment of multiple protein networks,” _Journal of Bioinformatics_ , vol. 25, no. 12, pp. i253–i258, 2009.
  * [375] Y. Xia, C.-Q. Xia, X. Pan, and H.-B. Shen, “Graphbind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues,” _Nucleic acids research_ , vol. 49, no. 9, pp. e51–e51, 2021.
  * [376] T. Gaudelet, B. Day, A. R. Jamasb, J. Soman, C. Regep, G. Liu, J. B. Hayter, R. Vickers, C. Roberts, J. Tang _et al._ , “Utilizing graph machine learning within drug discovery and development,” _Briefings in bioinformatics_ , vol. 22, no. 6, p. bbab159, 2021.
  * [377] J. Liu, G. Tan, W. Lan, and J. Wang, “Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks,” _BMC bioinformatics_ , vol. 21, no. 6, pp. 1–12, 2020.
  * [378] S. Parisot, S. I. Ktena, E. Ferrante, M. Lee, R. G. Moreno, B. Glocker, and D. Rueckert, “Spectral graph convolutions for population-based disease prediction,” in _Proceedings of ICMICCI_. Springer, ”2017”, pp. 177–185.
  * [379] A. Sanchez-Gonzalez, N. Heess, J. T. Springenberg, J. Merel, M. Riedmiller, R. Hadsell, and P. Battaglia, “Graph networks as learnable physics engines for inference and control,” in _Proceedings of ICML_ , ”2018”, pp. 4470–4479.
  * [380] S. Seo, C. Meng, and Y. Liu, “Physics-aware difference graph networks for sparsely-observed dynamics,” in _International Conference on Learning Representations_ , 2019.
  * [381] M. Prates, P. H. Avelar, H. Lemos, L. C. Lamb, and M. Y. Vardi, “Learning to solve np-complete problems: A graph neural network for decision tsp,” in _Proceedings of AAAI_ , vol. 33, no. 01, ”2019”, pp. 4731–4738.
  * [382] Y. Wang, Y. Tong, C. Long, P. Xu, K. Xu, and W. Lv, “Adaptive dynamic bipartite graph matching: A reinforcement learning approach,” in _Proceedings of ICDE_. IEEE, ”2019”, pp. 1478–1489.
  * [383] P. Tiwari, H. Zhu, and H. M. Pandey, “Dapath: Distance-aware knowledge graph reasoning based on deep reinforcement learning,” _Journal of Neural Networks_ , vol. 135, pp. 1–12, 2021.
  * [384] H. Peng, B. Du, M. Liu, M. Liu, S. Ji, S. Wang, X. Zhang, and L. He, “Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning,” _Journal of Information Sciences_ , vol. 578, pp. 401–416, 2021.
  * [385] T. Zhao and J. Wang, “Learning sequential distribution system restoration via graph-reinforcement learning,” _Journal of IEEE Transactions on Power Systems_ , vol. 37, no. 2, pp. 1601–1611, 2021.
  * [386] J. Park, J. Chun, S. H. Kim, Y. Kim, and J. Park, “Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning,” _Journal of Production Research_ , vol. 59, no. 11, pp. 3360–3377, 2021.
  * [387] Z. Liu, W. Zhang, Y. Fang, X. Zhang, and S. C. Hoi, “Towards locality-aware meta-learning of tail node embeddings on networks,” in _Proceedings of ACM_ , 2020, pp. 975–984.
  * [388] Z. Liu, Y. Fang, C. Liu, and S. C. Hoi, “Relative and absolute location embedding for few-shot node classification on graph,” in _Proceedings of AAAI_ , vol. 35, no. 5, 2021, pp. 4267–4275.
  * [389] F. Zhou, C. Cao, K. Zhang, G. Trajcevski, T. Zhong, and J. Geng, “Meta-gnn: On few-shot node classification in graph meta-learning,” in _Proceedings of CIKM_ , 2019, pp. 2357–2360.
  * [390] M. Chen, W. Zhang, W. Zhang, Q. Chen, and H. Chen, “Meta relational learning for few-shot link prediction in knowledge graphs,” in _Proceedings of EMNLP-IJCNLP_ , 2019.
  * [391] K. Huang and M. Zitnik, “Graph meta learning via local subgraphs,” in _Proceedings of NIPS_ , ”2020”.
  * [392] J. Chauhan, D. Nathani, and M. Kaul, “Few-shot learning on graphs via super-classes based on graph spectral measures,” in _Proceedings of ICLR_ , 2020.
  * [393] J. Kim, T. Kim, S. Kim, and C. D. Yoo, “Edge-labeling graph neural network for few-shot learning,” in _Proceedings of IEEE/CVF_ , 2019, pp. 11–20.
  * [394] T. Yu, S. He, Y.-Z. Song, and T. Xiang, “Hybrid graph neural networks for few-shot learning,” in _Proceedings of AAAI_ , vol. 36, no. 3, 2022, pp. 3179–3187.
  * [395] H. Zhu and P. Koniusz, “Ease: Unsupervised discriminant subspace learning for transductive few-shot learning,” in _Proceedings of IEEE/CVF_ , 2022, pp. 9078–9088.
  * [396] W. Feng, Y. Dong, T. Huang, Z. Yin, X. Cheng, E. Kharlamov, and J. Tang, “Grand+: Scalable graph random neural networks,” in _Proceedings of ACM_ , ”2022”, pp. 3248–3258.
  * [397] S. W. Min, K. Wu, M. Hidayetoglu, J. Xiong, X. Song, and W. mei Hwu, “Graph neural network training and data tiering,” in _Proceedings of SIGKDD_ , ”2022”, pp. 3555–3565.
  * [398] R. Ran, N. Xu, W. Wang, Q. Gang, J. Yin, and W. Wen, “Cryptogcn: Fast and scalable homomorphically encrypted graph convolutional network inference,” _arXiv preprint arXiv:2209.11904_ , 2022.
  * [399] M. Lin, W. Li, D. Li, Y. Chen, and S. Lu, “Resource-efficient training for large graph convolutional networks with label-centric cumulative sampling,” in _Proceedings of ACM_ , ”2022”, pp. 1170–1180.
  * [400] H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna, “Graphsaint: Graph sampling based inductive learning method,” 2020.
  * [401] D. Zheng, X. Song, C. Yang, D. LaSalle, and G. Karypis, “Distributed hybrid cpu and gpu training for graph neural networks on billion-scale heterogeneous graphs,” in _Proceedings of KDD_ , 2022, pp. 4582–4591.
  * [402] C.-C. M. Yeh, M. Gu, Y. Zheng, H. Chen, J. Ebrahimi, Z. Zhuang, J. Wang, L. Wang, and W. Zhang, “Embedding compression with hashing for efficient representation learning in large-scale graph,” in _Proceedings of SIGKDD_ , 2022, pp. 4391–4401.
  * [403] X. Zhao, J. Liang, and J. Wang, “A community detection algorithm based on graph compression for large-scale social networks,” _Journal of Information Sciences_ , vol. 551, pp. 358–372, 2021.
  * [404] Z. Liu, K. Zhou, F. Yang, L. Li, R. Chen, and X. Hu, “Exact: Scalable graph neural networks training via extreme activation compression,” in _Proceedings of ICLR_ , ”2021”.
  * [405] Z. Song, Y. Gu, J. Qi, Z. Wang, and G. Yu, “Ec-graph: A distributed graph neural network system with error-compensated compression,” in _Proceedings of ICDE_. IEEE, ”2022”, pp. 648–660.
  * [406] L. Huang, Z. Zhang, Z. Du, S. Li, H. Zheng, Y. Xie, and N. Tan, “Epquant: A graph neural network compression approach based on product quantization,” _Journal of Neurocomputing_ , vol. 503, pp. 49–61, 2022.
  * [407] L. Cui, X. Tang, S. Katariya, N. Rao, P. Agrawal, K. Subbian, and D. Lee, “Allie: Active learning on large-scale imbalanced graphs,” in _Proceedings of ACM_ , ”2022”, pp. 690–698.
  * [408] Z. Huang, S. Zhang, C. Xi, T. Liu, and M. Zhou, “Scaling up graph neural networks via graph coarsening,” in _Proceedings of KDD_ , ”2021”, pp. 675–684.
  * [409] Z. Zhao, Y. Zhang, and Z. Feng, “Towards scalable spectral embedding and data visualization via spectral coarsening,” in _Proceedings of ACM_ , ”2021”, pp. 869–877.
  * [410] A. Taghibakhshi, S. MacLachlan, L. Olson, and M. West, “Optimization-based algebraic multigrid coarsening using reinforcement learning,” _Journal of NIPS_ , vol. 34, pp. 12 129–12 140, 2021.
  * [411] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” _Advances in neural information processing systems_ , vol. 30, 2017.
  * [412] E. Strumbelj and I. Kononenko, “An efficient explanation of individual classifications using game theory,” _The Journal of Machine Learning Research_ , vol. 11, pp. 1–18, 2010.
  * [413] R. Agarwal, L. Melnick, N. Frosst, X. Zhang, B. Lengerich, R. Caruana, and G. E. Hinton, “Neural additive models: Interpretable machine learning with neural nets,” _Advances in Neural Information Processing Systems_ , vol. 34, pp. 4699–4711, 2021.
  * [414] D. Alvarez Melis and T. Jaakkola, “Towards robust interpretability with self-explaining neural networks,” _Advances in neural information processing systems_ , vol. 31, 2018.
  * [415] Z. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec, “Gnnexplainer: Generating explanations for graph neural networks,” _Advances in neural information processing systems_ , vol. 32, 2019.
  * [416] W. Lin, H. Lan, and B. Li, “Generative causal explanations for graph neural networks,” in _International Conference on Machine Learning_. PMLR, 2021, pp. 6666–6679.
  * [417] Y.-X. Wu, X. Wang, A. Zhang, X. He, and T.-S. Chua, “Discovering invariant rationales for graph neural networks,” _arXiv preprint arXiv:2201.12872_ , 2022.
  * [418] W. Lin, H. Lan, H. Wang, and B. Li, “Orphicx: A causality-inspired latent variable model for interpreting graph neural networks,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2022, pp. 13 729–13 738.
  * [419] H. Yuan, H. Yu, S. Gui, and S. Ji, “Explainability in graph neural networks: A taxonomic survey,” _IEEE Transactions on Pattern Analysis and Machine Intelligence_ , 2022.
  * [420] D. Luo, W. Cheng, D. Xu, W. Yu, B. Zong, H. Chen, and X. Zhang, “Parameterized explainer for graph neural network,” _Advances in neural information processing systems_ , vol. 33, pp. 19 620–19 631, 2020.
  * [421] E. Dai and S. Wang, “Towards self-explainable graph neural network,” in _Proceedings of the 30th ACM International Conference on Information & Knowledge Management_, 2021, pp. 302–311.
  * [422] Z. Liu, D. Zhou, and J. He, “Towards explainable representation of time-evolving graphs via spatial-temporal graph attention networks,” in _Proceedings of ACM_ , ”2019”, pp. 2137–2140.
  * [423] T. Schnake, O. Eberle, J. Lederer, S. Nakajima, K. T. Schutt, K.-R. Müller, and G. Montavon, “Higher-order explanations of graph neural networks via relevant walks,” _Journal of IEEE transactions on pattern analysis and machine intelligence_ , 2021.
  * [424] J. Klicpera, A. Bojchevski, and S. Günnemann, “Predict then propagate: Graph neural networks meet personalized pagerank,” _arXiv preprint arXiv:1810.05997_ , 2018.
  * [425] E. Chien, J. Peng, P. Li, and O. Milenkovic, “Adaptive universal generalized pagerank graph neural network,” _arXiv preprint arXiv:2006.07988_ , 2020\.
  * [426] M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li, “Simple and deep graph convolutional networks,” in _International Conference on Machine Learning_. PMLR, 2020, pp. 1725–1735.
  * [427] T. H. Do, D. M. Nguyen, G. Bekoulis, A. Munteanu, and N. Deligiannis, “Graph convolutional neural networks with node transition probability-based message passing and dropnode regularization,” _Expert Systems with Applications_ , vol. 174, p. 114711, 2021.
  * [428] H. Zeng, M. Zhang, Y. Xia, A. Srivastava, A. Malevich, R. Kannan, V. Prasanna, L. Jin, and R. Chen, “Decoupling the depth and scope of graph neural networks,” _Advances in Neural Information Processing Systems_ , vol. 34, pp. 19 665–19 679, 2021.
  * [429] W. Lu, Y. Zhan, Z. Guan, L. Liu, B. Yu, W. Zhao, Y. Yang, and D. Tao, “Skipnode: On alleviating over-smoothing for deep graph convolutional networks,” _arXiv preprint arXiv:2112.11628_ , 2021.
  * [430] C. Morris, M. Ritzert, M. Fey, W. L. Hamilton, J. E. Lenssen, G. Rattan, and M. Grohe, “Weisfeiler and leman go neural: Higher-order graph neural networks,” in _Proceedings of the AAAI_ , vol. 33, no. 01, ”2019”, pp. 4602–4609.
  * [431] H. Maron, H. Ben-Hamu, H. Serviansky, and Y. Lipman, “Provably powerful graph networks,” _Advances in neural information processing systems_ , vol. 32, 2019.
  * [432] A. Wijesinghe and Q. Wang, “A new perspective on” how graph neural networks go beyond weisfeiler-lehman?”,” in _International Conference on Learning Representations_ , 2021.
  * [433] M. Zhang and P. Li, “Nested graph neural networks,” _Advances in Neural Information Processing Systems_ , vol. 34, pp. 15 734–15 747, 2021.
  * [434] J. You, J. M. Gomes-Selman, R. Ying, and J. Leskovec, “Identity-aware graph neural networks,” in _Proceedings of the AAAI Conference on Artificial Intelligence_ , vol. 35, no. 12, 2021, pp. 10 737–10 745.
  * [435] V. G. Satorras, E. Hoogeboom, and M. Welling, “E(n) equivariant graph neural networks,” in _International conference on machine learning_. PMLR, 2021, pp. 9323–9332.
  * [436] S. Batzner, A. Musaelian, L. Sun, M. Geiger, J. P. Mailoa, M. Kornbluth, N. Molinari, T. E. Smidt, and B. Kozinsky, “E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials,” _Nature communications_ , vol. 13, no. 1, pp. 1–11, 2022.
  * [437] W. Du, H. Zhang, Y. Du, Q. Meng, W. Chen, N. Zheng, B. Shao, and T.-Y. Liu, “Se(3) equivariant graph neural networks with complete local frames,” in _International Conference on Machine Learning_. PMLR, 2022, pp. 5583–5608.
  * [438] N. Keriven and G. Peyré, “Universal invariant and equivariant graph neural networks,” _Journal of NIPS_ , vol. 32, pp. 7092–7101, 2019.
  * [439] P. B. Jrgensen and A. Bhowmik, “Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids,” _npj Computational Materials_ , vol. 8, no. 1, pp. 1–10, 2022.
  * [440] E. Dai and S. Wang, “Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information,” in _Proceedings of WSDM_ , 2021, pp. 680–688.
  * [441] Y. Dong, N. Liu, B. Jalaian, and J. Li, “Edits: Modeling and mitigating data bias for graph neural networks,” in _Proceedings of the ACM Web Conference 2022_ , 2022, pp. 1259–1269.
  * [442] W. Song, Y. Dong, N. Liu, and J. Li, “Guide: Group equality informed individual fairness in graph neural networks,” in _Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_ , 2022, pp. 1625–1634.
  * [443] T. Zhang, T. Zhu, M. Han, F. Chen, J. Li, W. Zhou, and P. S. Yu, “Fairness in graph-based semi-supervised learning,” _Knowledge and Information Systems_ , pp. 1–28, 2022.
  * [444] Z. Zhang, Q. Liu, Z. Huang, H. Wang, C.-K. Lee, and E. Chen, “Model inversion attacks against graph neural networks,” _IEEE Transactions on Knowledge and Data Engineering_ , 2022.
  * [445] I.-C. Hsieh and C.-T. Li, “Netfense: Adversarial defenses against privacy attacks on neural networks for graph data,” _IEEE Transactions on Knowledge and Data Engineering_ , 2021.
  * [446] C. Wu, F. Wu, L. Lyu, T. Qi, Y. Huang, and X. Xie, “A federated graph neural network framework for privacy-preserving personalization,” _Nature Communications_ , vol. 13, no. 1, pp. 1–10, 2022.
  * [447] G. Yang, J. Cao, Z. Chen, J. Guo, and J. Li, “Graph-based neural networks for explainable image privacy inference,” _Pattern Recognition_ , vol. 105, p. 107360, 2020.
  * [448] H. Tian, X. Zheng, X. Zhang, and D. D. Zeng, “$\epsilon$-k anonymization and adversarial training of graph neural networks for privacy preservation in social networks,” _Electronic Commerce Research and Applications_ , vol. 50, p. 101105, 2021.