# Don’t follow the leader: Independent thinkers create scientific innovation

Sean Kelty  Department of Physics & Astronomy, University of Rochester,
Rochester, NY 14607, USA  Raiyan Abdul Baten  Department of Computer Science,
University of Rochester, Rochester, NY 14607, USA  Adiba Mahbub Proma
Department of Computer Science, University of Rochester, Rochester, NY 14607,
USA  Ehsan Hoque  Department of Computer Science, University of Rochester,
Rochester, NY 14607, USA  Johan Bollen  Luddy School of Informatics, Computing
and Engineering, 919 E. 10th St., Bloomington, IN 47408, USA  Gourab Ghoshal
<EMAIL_ADDRESS>Department of Physics & Astronomy, University of
Rochester, Rochester, NY 14607, USA  Department of Computer Science,
University of Rochester, Rochester, NY 14607, USA

###### Abstract

Academic success is distributed unequally; a few top scientists receive the
bulk of attention, citations, and resources. However, do these “superstars”
foster leadership in scientific innovation? We introduce three information-
theoretic measures that quantify novelty, innovation, and impact from
scholarly citation networks, and compare the scholarly output of scientists
who are either not connected or strongly connected to superstar scientists. We
find that while connected scientists do indeed publish more, garner more
citations, and produce more diverse content, this comes at a cost of lower
innovation and higher redundancy of ideas. Further, once one removes papers
co-authored with superstars, the academic output of these connected scientists
diminishes. In contrast, authors that produce innovative content without the
benefit of collaborations with scientific superstars produce papers that
connect a greater diversity of concepts, publish more, and have comparable
citation rates, once one controls for transferred prestige of superstars. On
balance, our results indicate that academia pays a price by focusing attention
and resources on superstars.

##  I Introduction

“To truly make an apple pie from scratch you must first invent the universe”—a
quote attributed to Carl Sagan Cliff_2021 —illustrates the idea that the
process by which individuals create is contingent upon the elements on which
that creation is based. Whether creating a new piece of music, going about
daily routines, or engaging in scientific research, people’s actions are
founded in the information, experiences, and relationships that they have
establish by themselves and through others McAndrew:2014yi ; Muller:2019ny ;
Hazarie:2020zb ; Chen:2022tt . Each person has their own basis of knowledge
that stems from their own lived experiences while also existing in a network
of relationships through which they share experiences and knowledge with each
other, thereby informing a collective understanding among a network of
connected individuals Rodriguez_2016 . Within such networks, hierarchies can
emerge in which some actors exert greater social influence over the network
and thus the creative process that it supports, while others may influence
only those closest to them or no one at all Holme:2006bf . This social
hierarchy is common in the societal dynamics of government and politics, where
some individuals and institutions exert a great degree of influence over the
flow of information in the system and opinion formation Ghoshal:2007la ;
Recuero:2019jp ; Dubois:2014ch .

Academia is not immune from the emergence of social hierarchies; some
academics can function as figures of authority due to the merit and influence
of their work and their prominent position in a network of academic
collaborations. Citations as an indicator of academic influence
RADICCHI2017704  have long been known to be distributed very
unequallyhindex:hirsch2005 , with a minority of a few scientists receiving
most citations. Such inequality may be increasing at a global levelglobal-
citation-inequality , at least with respect to citation numbers. In academic
publishing, biasing effects like this have been studied under the lens of the
Matthew Effect, where success begets more success and early success compounds
into a cumulative advantage as the “rich get richer” Matthew-Effect . There
are arguments that this effect is beneficial for academia; the rewards of top
researchers are proportional to their contributions, which ensures the
“epistemic security” of the field encyclopedia-creativity . This thinking is
aligned with the notion that science should operate as a meritocracy; those
who contribute the most are also valued the most, and will therefore be most
influential. Indeed, there is a high degree of trust in our most successful
academics and the value of their mentorship. For instance, junior researchers
collaborating with top scientists at the early stages of their career are
likely to become top-cited scientists themselves, especially those at less
prestigious universities top-scientist-collab-success . Inexperienced
academics can benefit from apprenticeships with top scientists; the
“chaperoning” of early-career scientists leads to higher rates of publication
in high-impact journals chaperone-effect . These relationships are frequently
mutually beneficial. Less visible authors benefit from more opportunities to
publish papers in high quality journals that attract larger audiences, whereas
top scientists gain collaborators with unique skills to produce more high
quality work coauthorship-top-ordinary . Close collaboration of less visible
academics with those in the upper echelons can furthermore create
opportunities for a first-mover advantage, inducing a positive feedback loop
and early bandwagoning of innovative ideas Abrahamson-innovation-diffusion .

While top academics (sometimes referred to as “superstars”) may make
consistent and high impact contributions that benefit their field and
collaborators, their status as superstars may also have deleterious effects
due to the subsequent concentration of resources and attention. For instance,
it has been shown that the collaborators of academic superstars experience a 5
to 9$\%$ drop in publication rates after the sudden death of that superstar
Superstar-Extinction , highlighting their dependence on the superstar’s
collaboration. In fact, it is unclear whether collaborating with superstars
truly fosters independent career development Clauset:eu ; Janosov:2020ri
Furthermore, superstars can induce a high degree of inequality in the
distribution of research funding due to a funding Matthew-effect. Those who
receive funding accumulate twice as much research funding afterwards compared
to those who submitted similarly valued proposals but found themselves, by
chance, just below the funding threshold. There is no evidence that this
accumulation of research funding is due to actual achievements enabled by
previous funding Matthew-Effect-Science-Funding ; Matthew-Effect-Model . If
successful collaborations with superstars lead to early funding success, this
can induce a superstar-fueled funding cycle that increasingly widens the gap
between scientific haves and have-nots.

The topology, structure, and characteristics of scientific collaboration
networks may play an important role in these effects since they shape both the
production and dissemination of ideas, potentially with conflicting outcomes.
Tightly connected networks could be more efficient in distributing and
leveraging knowledge thereby yielding higher productivity, but may at the same
time lead to a decline of diversity, reducing exploration and discovery
exploitation-exploration-Lazer ; Rodan-Heterogeneity-Innovation ; Chang-
knowledge-diffusion-imitation-innovation . Although some spillover effects may
occur, i.e. collaborators of highly-acclaimed authors benefit by proxy
Novelty-Recognition-MatthewEffect , it is not clear whether the concentration
of attention of resources towards superstars yields more novel and innovative
research. This is a particularly relevant issue with the rise of
interdisciplinary research which relies on the ability of scientists to
collaborate in equitable teams that foster creativity and innovation across
various research fields flat:evans2022 .

To investigate the effects of superstar influence on academic productivity,
impact, and innovation, we perform a comprehensive analysis of the American
Physical Society corpus. Following Superstar-Extinction , we define superstars
as academics who are among the top .1% in terms of their h-index h-index-
justification ; h-index . We extract the semantic content of over 250,000
abstracts, defining a number of information-theoretic measures to quantify the
novelty and innovation of each paper. We augment this with analysis of
publication and citation rates, and examine the difference in academic output
between researchers who collaborate with or cite frequently papers by
superstars against those with little-to-no connection to such superstars. We
find that at the individual level, collaborators and frequent citers of
superstars, publish more, garner higher citations and produce papers with more
diverse content compared to other academics. However, their work is no more
innovative than the rest of the corpus and its content is more redundant.
Further, once one excludes papers co-authored with superstars, their
publication and citation output are no different from the rest of the corpus
and in some cases output is lower.

Focusing on early career researchers, we find that those who frequently
collaborate with superstars in the beginning of their careers, do eventually
go on to produce impressive academic output, although once the collaboration
is removed, their output in terms of publication rates, citation impact, and
innovation is significantly diminished. On the other hand, early career
researchers that produce innovative content without the benefit of early
superstar collaboration, continue to produce such content over the rest of
their careers. They publish more then early collaborators of superstars and
accrue similar citation numbers, once one controls for the collaboration
itself.

##  II Results

###  II.1 Data

We use the American Physical Society (APS) corpus APS-dataset  that contains
articles published in APS journals since 1893. The data set contains full
citation data, i.e. the citations pointing from the references of one article
to another, allowing a reconstruction of the full citation network among all
articles, including article-specific fields such as DOI, journal, volume,
issue, first page and last page OR article id and number of pages, title,
authors, affiliations, publication history, PACS codes, table of contents
heading, article type, and copyright information. Given that the data does not
include article abstracts, we used a web-scraping algorithm beautiful-soup  to
collect abstracts for 250,628 articles corresponding to between 35-40% of all
published papers across the different APS journals (Fig. S1). We note that
around 1% of these articles have references not contained in the APS citation
network, and on average we scraped abstracts for 38% of paper references. The
distribution of citations and h-index are both heavy-tailed (Fig. S2), with
the average number of citations being 14.4 and the average h-index 1.74.
Author disambiguation was done using a rule-based scoring method author-
disambiguation  (Cf. Sec.S1.2) We consider authors who first publish on or
after 1970, and define superstars as those with the top .1% of h-index in the
corpus, corresponding to an h-index threshold of 21. This yields 303
superstars among 292,394 authors. The summary statistics can be found in Tab.
S1.

In order to extract topics from the collected abstracts, we use an
unsupervised Latent Dirichlet Allocation (LDA) algorithm on phrases (P-LDA)
PLDA  to establish vector embeddings for phrases and documents within our
corpus. Stop words in the corpus were removed, all words were lemmatized, and
phrases were determined based on a significance score that determined whether
or not phrases occurred due to random chance. These vector embeddings have
dimensionality $k$ correspoding to the number of topics defined for our
corpus. P-LDA utilizes Gibbs Sampling to generate distributions of topics over
phrases as well as documents Lee:2022tf , from which novelty scores can be
extracted based on topic-spread. We choose a number of topics $k$ based on the
UMass coherence measure (UMASS-Coherence ), the value of which first
stabilizes at $k=25$ topics (Fig. S3). Tab. S2 shows the top 10 terms per
topic. The resulting output for each document $u$ is a $k$-dimensional vector
${\bf v}^{u}$ whose elements $v^{u}_{i}$ correspond to the frequency of topic
$i$ extracted from its abstract (example in Tab. S3).

###  II.2 Novelty, innovation and redundancy

Novelty detection in the literature has been implemented in a variety of ways
Novelty-Detection-Review , such as contextualizing novelty in machine learning
as information retrieval Novelty-Detecion-TREC ; Ghoshal-Novelty-Detection ,
distant combinations of ideas via citation relations Uzzi-Atypical-Recomb ,
first-pass combinations of concepts never before connected Schumpeter:1982 ,
knowledge-graphs of concepts within social networks Rodan-Heterogeneity-
Innovation , and agent-based simulations of social and individual learning
Chang-knowledge-diffusion-imitation-innovation .

Here we rely on document-level embeddings that represent a distribution of all
topics contained within the abstract of given paper, using which one can
define the topic diversity in terms of a paper, its references, and articles
that cite the paper. Using this, we define a variety of metrics capturing
different aspects of novelty and innovation.

Coupling connections between authors and the content of their works can then
elucidate the influence that superstars have on the success of and novelty
produced by other academics.

Entropy: For a given document $u$, we define the Shannon entropy as

$I^{(S)}_{u}=-\sum^{k}_{i=1}v^{u}_{i}\ln v^{u}_{i},$ (1)

The expression quantifies the average level of “surprise” or uncertainty over
the outcomes of a random variable Information-Theory-Book . In this context,
papers focusing on limited number of topics in abstracts will yield low values
of $I^{(S)}_{u}$, whereas those with a wide diversity of topics will yield a
larger value of the entropy.

Reference and Citation Diversity: While $I^{(S)}_{u}$ measures the “surprise”
with respect to a paper’s content, in this case its abstract, references and
citations refer to the degree that the ideas in a given paper were inspired by
other papers (references) or of inspiration to other papers (citations). We
can thus measure the novelty of a paper, or its Information Diversity Aral-
Dhillon , by evaluating the dispersion of the topics of its references or the
citations its receives. The greater the variance of the topic distribution,
the higher the information diversity. For a set $X^{u}$, that can represent
either the references in paper $u$, or citations to paper $u$, we define the
quantity,

$I^{(X)}_{u}=\frac{1}{|X^{u}|}\sum_{l\in X^{u}}\left[1-\cos\left({\bf
v}^{l},\overline{X^{u}}\right)\right]$ (2)

where $\cos\left({\bf v}^{l},\overline{X^{u}}\right)$ is the cosine similarity
of the vector embedding of a particular reference/citation ${\bf v}^{l}$ with
the average over the vector embeddings of all references/citations in the set
$X^{u}$. We can as such define _reference diversity_ and _citation diversity_
as the information diversity over the references from a paper and citations to
the paper respectively.

Innovation: The metrics defined thus far are based on topic models expressed
as topic distributions per document derived from the words in their content
(abstracts). These metrics capture topic diversity of the paper itself, or its
influences, but does not express the degree to which the paper expanded the
literature through innovation. In other words, they express what document
themselves are about, but not whether this adds to the diversity of the
literature. We therefore define Innovation as the degree to which the document
adds topics in new combination to the literature Kuhn1962 ; diversity-
innovation-paradox . Specifically, innovation in this context, is a
measurement of when terms were first introduced or combined in the corpus (cf.
Sec. S1.4 and Fig. S4). Coupled with the novelty measures, this allows us to
track how the diversity of ideas correlates with new conceptual recombinations
and co-occurrences of terms. Following this logic, we define the
Innovativeness of paper $u$ as

$I_{u}^{(I)}=\frac{1}{2}\sum_{w_{1}\neq w_{2}\in u}\mathcal{I}(w_{1},w_{2};u)$
(3)

where $w_{1}$ and $w_{2}$ are distinct terms in paper $u$,
$\mathcal{I}(w_{1},w_{2};u)$ is an indicator function that is $1$ if terms
$w_{1}$ and $w_{2}$ are first seen within the corpus in paper $u$ and 0
otherwise, and the $\frac{1}{2}$ prefix accounts for double counting. To
remove spurious conceptual links due to chance or extreme rarity, we calculate
a point-wise mutual information for all links as the log ratio of co-
occurrence probability over the individual probabilities of each concept
diversity-innovation-paradox . In Fig. S5 we determine the Pearson’s $r$
correlation coefficients between each measure and find only weak correlations,
indicating that each measure captures a different aspect of academic output.

Redundancy: Finally, in a related context, in the field of creative ideation,
it has been reported that inspirees stimulated by highly creative alters, tend
to generate more creative ideas Baten-divergent-thinking ; Baten_2021 ;
Baten_2022 . However, as a group, the inspirees ideas was found to be similar
to each other leading to redundancy in generated ideas over time at the group
level. To check whether a similar effect manifests in academic publishing, we
compute the cosine similarity score between papers $u,u^{\prime}$ in the set
$P(G,s,t)$ thus

$\textrm{Sim}(G,s,t)=\frac{2}{\left|P(G,s,t)\right|(\left|P(G,s,t)\right|-1)}\sum_{u,u^{\prime}\in
P(G,s,t)}\cos({\bf v}^{u},{\bf v}^{u^{\prime}}).$ (4)

###  II.3 Superstar statistics

We next examine whether the novelty and innovation produced by superstars are
significantly different from the rest of the academic corpus. In Fig. 1 we
plot the Reference and Citation diversity (Eq. (2)), the Shannon entropy (Eq.
(1)) and Innovation (Eq. (3)) comparing the set of superstar academics against
the rest of the authors in the corpus. In terms of reference diversity,
citation diversity and Shannon entropy, superstars outperform the remaining
academics by $20\%$, $15\%$, and $2\%$ respectively. That is, superstars are
inspired by a higher diversity of content, publish works that are more
conceptually diverse, and inspire a wider array of publications than non-
superstars. The starkest contrast can be seen in terms of Innovation, where
there is a factor of ten difference between superstars and other academics
indicating that the former are more prolific in introducing new combinations
of terms. We note that there is a monotonic dependence of the metrics with
number of publications for all academics, although the effect is more
pronounced for superstars (Fig. S6). Furthermore, there is also a monotonic
dependence of citations received by a paper $u$ and the novelty/innovation
metrics (once again more pronounced for superstars) indicating that an
increase in conceptual diversity and the ability to connect concepts for the
first time is rewarded in terms of more attention paid to that paper (Fig.
S7).

Figure 1: Average author-level statistics of novelty and innovation A
Reference Diversity, B Citation Diversity, C Shannon Entropy, D Innovation.
The orange bar is for superstars (h-index $\geq$ 21) and the blue bars
correspond to all other authors in the corpus.

###  II.4 Superstar influence

Having established that superstars outperform other academics in terms of our
metrics, we next determine to what degree superstars affect the academic
output of their collaborators and their “inspirees” (those inspired by their
work). Inspirees are authors that cite a superstar’s papers, for whom we
determine the degree of inspiration by the frequency of citations. We examine
inspirees both at the group- and individual-levels. At the group-level, we
center the superstar in a network of inspirees where the degree of inspiration
is the number of times a researcher cites the superstar. We then partition the
inspirees into groups based on their degree of inspiration, where the upper
bounds for each bin are the top 10$\%$ of inspirees, 20$\%$, 30$\%$, 50$\%$,
and 100$\%$. These groups represent increasingly weakening ties to a given
superstar; those in the top 10 percent are the most actively inspired, while
the bottom 50 percent typically cite the superstar only once. Note that some
inspirees in the bottom 50 group of one superstar may be in the top group of
another superstar. The increasing bin sizes are chosen to account for the
decreasing frequency of inspired citations among the least-inspired inspirees,
such that there are sufficient number of papers compared between groups.

Figure 2: Novelty and Innovation statistics at the group-level Temporal
trajectory of average paper-level statistics. A: Shannon Entropy, B:
Innovation, C: Citations per-paper. Aggregated group-level statistics D:
Shannon Entropy, E: Innovation, F: Citations per-paper. Curves indicate
averages, shaded area 95% confidence interval.

Given that we are interested in the temporal evolution of superstar influence
on the novelty and innovation of the inspirees, we denote the year of the
first superstar publication as $t_{0}=0$ and for every susbsequent year
$t>t_{0}$, we consider the set of publications by the inspirees who cite the
superstar. For each partitioned group, we calculate the average novelty of all
of the publications in year $t$ per partition. Denoting the set of papers
inspired by superstar $s$ for partition $G$ at year $t$ as $P(G,s,t)$, the
average novelty scores are computed as

$\langle I_{u}^{(l)}\rangle_{G,s,t}=\frac{1}{|P(G,s,t)|}\sum_{u\in
P(G,s,t)}I_{u}^{(l)}$ (5)

where $l=S,X,I$ is the novelty or innovation score of paper $u$.

We plot the results of our analysis in Fig. 2. In terms of the temporal
evolution of the Shannon entropy, while there is a monotonic
increase—reflecting an increase in the body of knowledge with time (Fig.
S8)—we find little-to-no differences across the groups as seen in Fig. 2A.
Averaging over the entire temporal range also indicates a flat trend (Fig.
2D). Similar trends are seen for the reference diversity both in terms of its
temporal evolution (upper panel of Fig. S9A,B) as well as their temporally
averaged values (lower panel). Unlike the entropy or reference diversity,
there is a decreasing trend in time for the citation diversity. We observe a
5% decrease in the measure between those in the top 10% as compared to the
bottom 50%. Figure 2B,E indicates the same trend for Innovation which also
decreases in time across all groups, reflecting a saturation in the number of
combinations of new terms that are combined by authors as their career
progresses. The difference between the top and bottom groups is now around
15%. Finally, citations to papers experience an initial boost and then
decreases in time as seen in Fig. 2C, with now much clearer differences
between the groups. Indeed, there is a 40% difference in citations per-paper
between the most and least inspired groups as seen in Fig. 2F.

In terms of redundancy, in Fig. S9C we plot the cosine similarity (Eq. (4). As
the figure indicates, across all groups there is a decreasing trend in the
temporal evolution of the similarity, yet a clear difference exists, whereby
papers published by the top 10% are on average 8% more similar to each other
in terms of content when compared to the bottom 50%. Taken together, the
results indicate that groups of authors who cite superstar papers often do get
a citation boost as compared to other sets of authors. However, their output
is modestly more innovative and equally novel as compared to the rest of the
corpus. Rather their content is more redundnant than the remaining sets of
authors.

Figure 3: Novelty and Innovation statistics at the individual author-level. A
Reference Diversity, B Citation Diversity, C Shannon Entropy, D Innovation, E
Average citation count, F Average publication count.

Next, we dis-aggregate the group-level results and examine the degree of
superstar influence at the individual author level. In Fig. 3 we plot the
averages of the novelty and innovation metrics as well as citations and
publication counts across authors as a function of the fraction of their
papers that cite superstars. Given that many authors co-publish with
superstars, the blue curve indicates the results when including such papers,
while the orange curve shows the results excluding these papers. Figure 3A-C
indicate that as authors cite more superstars they experience an increase in
reference and citation diversity as well as the Shannon entropy irrespective
of whether one includes their collaboration with superstars. While we see no
indications of novelty of content being driven by superstar-influence at the
group-level, at the individual level the benefits are clear. On the other
hand, when looking at Innovation (Fig. 3D), the trend is either flat when
including all papers, and decreasing when co-authored publications are
excluded. Indeed, it appears that the more authors cite superstars, the _less
innovative_ their own publications become (i.e those not co-authored with a
superstar). The benefit of collaborating with a superstar becomes even more
apparent when looking at citations (Fig. 3E) and number of publications (Fig.
3 F). For the former when including collaborations there is a dramatic benefit
in terms of garnered citations (approximately 67% more citations on average)
that drops considerably when excluding collaborations. Indeed, the citation-
benefit appears to be driven primarily by being collaborators of superstars
who by definition have the largest number of citations to their papers. The
same appears to be the case for the latter, with the number of publications
increasing when including collaborations, and decreasing when excluded.

###  II.5 Early Collaborators and Early Innovators

Figure 4: Citations and Innovation for frequent collaborators and early
innovators A Citations per paper when including superstar papers, B The same
when excluding superstar papers. C Temporal evolution of Innovation. D The
same when excluding superstar papers. The horizontal axis $t-t_{0}$ indicates
the time elapsed from the $t_{0}$ the time of first publication for authors in
either group.

The results thus far provide evidence for academics inspired by superstars
producing output with diverse content and that receives visibility via
citations, while not necessarily being innovative in the sense of tying
together new concepts. On the other hand, there is also evidence that these
features are significantly boosted by direct collaboration with superstars,
and when left to their own devices their publication output, novelty and
innovation is lower than the rest of the corpus. Indeed, it begs the question
whether superstars foster independent individual success, or rather inhibits
it? For instance, as shown, at the aggregate level, the group of authors that
cite superstars the most often tend to publish on mostly the same topics.

To further probe this we restrict our analysis to early-career scientists.
Given that findings from prior studies have shown that collaboration with
successful scientists provides a boost for early career researchers top-
scientist-collab-success , and that early success generates a cumulative
advantage of long-term career success Matthew-Effect , we define _early
collaborators_ as those authors who collaborate with superstars in at least
half of their papers in the first five years of their career. As a point of
comparison, we define another set of authors who do not collaborate with, or
cite superstar papers, but are in the top 10% of the corpus in terms of
Innovation as measured by their first five years of publications. We term
these authors _early innovators_. We use innovation as a metric, given that
this is the measure by which superstars outperform other academics the most
(Fig. 1D) and therefore might serve as a robust indicator of academic
potential.

For academics in each group we track the temporal evolution of the citations
per-paper, the number of publications, as well as the Innovation, measured
from the date of first publication $t_{0}$ for authors in either group. Early
collaborators get more citations per paper (Fig. 4A) and publish more than
early innovators (Fig. S10A) particularly within the first ten years of their
career. However, when one removes superstar publications, the trend reverses
where now early innovators publish more (Fig. S10B) and garner a comparable
rate of citations as the other group (Fig. 4B ). Additionally the early
innovators maintain a higher degree of Innovation throughout their careers as
compared to early collaborators (Fig. 4C, D) with or without including
collaborations to superstars. Thus the evidence suggests that while early
career scientists indeed get a boost from collaborating with superstars, their
own academic output is less innovative and equally visible in terms of
citations, as compared to other early career scientists who produce innovative
output without the benefit of such collaborations.

##  III Conclusion and Discussion

In the exponentially growing knowledge-base of academia in which visibility
and funding are increasingly being biased towards top academics and
institutions, we examine the influence that superstar academics have on the
community as a whole and in terms of novelty and career success. Superstars
provide an irreplaceable source of novel ideas and contributions at rates that
exceed those of other academics in the corpus; our metrics support that their
accolades are well deserved and should be rewarded as such. We find superstars
are highly novel and inspire a higher diversity of concepts among their
followers and collaborators. However they do inhibit innovation potential.
Those academics most inspired by a superstar are individually themselves more
diverse in their papers, but at the group level add little intrinsic novelty
than groups more weakly inspired by the superstar, even though they achieve
higher citations.

Additionally, we find indications of a strong Matthew Effect whereby academics
who cite a superstar highly receive higher citations when collaborating with
the superstar than without, despite higher gains in concept diversity than
academic counterparts. Though collaboration with successful academics can
stimulate a successful career path, we find these collaborations can stifle
innovation and may not provide the best indicator of long-term independent
career success.

Collaboration is a requirement to tackle increasingly difficult
interdisciplinary problems. Superstars are well-positioned to foster
interdisciplinary research efforts by supporting early-career researchers.
Although the latter receive a citation boost when collaborate with a
superstar, this does not imply that they are developing more novel work than
their colleagues who are less connected to top academics. In fact, our results
indicate that those closest to a superstar show the lowest innovation
potential. This is slightly surprising given that the literature have shown
junior researchers that collaborate with superstars are more likely to publish
in high quality journals and have increased chances of engaging in high
quality research with other top scientists. On balance, however, we find that
this does not stimulate long term independent career success. This could be an
indication of individuals getting lost in the wake of a superstar, meaning
these researchers “bandwagon” off the ideas and visibility of their respective
superstars and iterate on the superstar’s work. Although there is value in
iterating upon already developed research questions, this may not foster
innovative work and stimulate individual careers. Indeed, very recently it has
been shown that there is a decline in disruptive ideas in both scientific
publications and patents Park_2023 . The authors attribute this to an ever
increasing reliance on a narrower set of extant scientific knowledge on which
to build ideas, a finding very much in line with our observation that
followers of superstars produce redundant and less innovative content as a
group.

The observed effects could be a consequence of superstars’ strong hold over
their respective fields. It’s been shown that paradigm shifts in thinking
occur after the sudden deaths of superstars. Collaborators of superstars
suffer a drop in publication rate after their superstar death, and the field
may experience a surge of contributions by outsiders who are
disproportionately likely to be highly-cited Azoulay-funeral . One can infer
that collaborators of superstars are successful because they are collaborating
with superstars. Care should be taken when considering these proteges
themselves for matters of funding and academic hiring. If the goal is to
foster highly novel work, elements outside of prestige and social connection,
such as efficacy, equity, and innovation, should be considered.

Our findings are not limited solely to early innovators, collaborators, and
inspirees. Though we provide early innovators as an example, many other groups
mining:bing2013  can be isolated and studied in the way we have done here to
identify promising academics based on early signatures of novelty or a range
of social parameters. We outlined multiple different definitions of novelty in
the introduction which we have not further developed in this study.
Implementing the different definitions and distinguishing different types of
novelty can elucidate what types of novelty are stifled or enhanced by
different social configurations.

A subject that we have not probed but is directly relevant to our discussion
is the matter of funding. In recent times, funding has increasingly become
more biased towards top institutions funding-bias-canada , with 90$\%$ of NSF
funding in 2018 going to 22$\%$ of funded institutions, serving 43$\%$ of all
institutions and 34$\%$ of underrepresented minorities APS-Report . This is
coupled with a history of funding disparities with respect to race and
underrepresented communities funding-inequalities ; racial-disparity-NSF ;
racial-disparity . Additionally, underrepresented groups produce novel works
at higher rates yet are taken up by other scholars at lower rates than novel
contributions by gender and racial majorities diversity-innovation-paradox .
Equitable funding programs have been shown to enhance research infrastructure,
investigator capabilities, and intra- and inter-university collaborations at
less prominent institutions EPSCoR . As we have shown, those that are least
influenced by superstars innovate the most and consequently have higher
citation rates. Coupling these results with added attention to equitable
funding practices funding:bollen2014  we believe will reduce the growing
inequality in academia and stimulate novel and innovative research.

Finally, we note that our investigation necessarily comes with limitations.
Given our sole focus on the APS body of literature, one should be careful to
extrapolate this to other academic disciplines. This is also an incomplete
subset of the entire journal, so a full corpus with an entire citation network
would give a more accurate picture.

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