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Background Growing evidence shows that scientific collaboration plays a crucial role in transformative innovation in the life sciences. For example, contemporary drug discovery and development reflects the work of teams of individuals from academic centers, the pharmaceutical industry, the regulatory science community, health care providers, and patients. However, public understanding of how collaborations between academia and industry catalyze novel target identification and first-in-class drug discovery is limited. Results We perform a comprehensive network analysis on a large scientific corpus of collaboration and citations (97,688 papers with 1,862,500 citations from 170 million scientific records) to quantify the success trajectory of innovative drug development. By focusing on four types of cardiovascular drugs, we demonstrate how knowledge flows between institutions to highlight the underlying contributions of many different institutions in the development of a new drug. We highlight how such network analysis could help to increase industrial and governmental support, and improve the efficiency or accelerate decision-making in drug discovery and development. Conclusion We demonstrate that network analysis of large public databases can identify and quantify investigator and institutional relationships in drug discovery and development. If broadly applied, this type of network analysis may help to enhance public understanding of and support for biomedical research, and could identify factors that facilitate decision-making in first-in-class drug discovery among academia, the pharmaceutical industry, and healthcare systems.

Significance Mentorship is arguably a scientist’s most significant collaborative relationship; yet of all collaborations, comparatively little research exists on the link between mentorship and protégé success. Using new large-scale data from the genealogical and performance records of 10s of thousands of scientists worldwide from 1960 to the present, we found that mentorship is associated with diverse forms of protégé success, significantly increasing protégés’ chances of producing celebrated research, being inducted into the National Academy of Science, and achieving superstardom. Paradoxically, protégés achieve their highest impact when they display intellectual independence from their mentors. Protégés do their best work when they break from their mentor’s research topics and coauthor no more than a small portion of their overall research with their mentors., Einstein believed that mentors are especially influential in a protégé’s intellectual development, yet the link between mentorship and protégé success remains a mystery. We marshaled genealogical data on nearly 40,000 scientists who published 1,167,518 papers in biomedicine, chemistry, math, or physics between 1960 and 2017 to investigate the relationship between mentorship and protégé achievement. In our data, we find groupings of mentors with similar records and reputations who attracted protégés of similar talents and expected levels of professional success. However, each grouping has an exception: One mentor has an additional hidden capability that can be mentored to their protégés. They display skill in creating and communicating prizewinning research. Because the mentor’s ability for creating and communicating celebrated research existed before the prize’s conferment, protégés of future prizewinning mentors can be uniquely exposed to mentorship for conducting celebrated research. Our models explain 34–44% of the variance in protégé success and reveals three main findings. First, mentorship strongly predicts protégé success across diverse disciplines. Mentorship is associated with a 2×-to-4× rise in a protégé’s likelihood of prizewinning, National Academy of Science (NAS) induction, or superstardom relative to matched protégés. Second, mentorship is significantly associated with an increase in the probability of protégés pioneering their own research topics and being midcareer late bloomers. Third, contrary to conventional thought, protégés do not succeed most by following their mentors’ research topics but by studying original topics and coauthoring no more than a small fraction of papers with their mentors.

Polarization affects many forms of social organization. A key issue focuses on which affective relationships are prone to change and how their change relates to performance. In this study, we analyze a financial institutional over a two-year period that employed 66 day traders, focusing on links between changes in affective relations and trading performance. Traders’ affective relations were inferred from their IMs (>2 million messages) and trading performance was measured from profit and loss statements (>1 million trades). Here, we find that triads of relationships, the building blocks of larger social structures, have a propensity towards affective balance, but one unbalanced configuration resists change. Further, balance is positively related to performance. Traders with balanced networks have the “hot hand”, showing streaks of high performance. Research implications focus on how changes in polarization relate to performance and polarized states can depolarize., How do socially polarized systems change and how does a change in polarization relate to performance? Using instant messaging data and performance records from day traders, the authors find that certain relations are prone to balance and that balance is associated with better trading decisions.

Fast growing scientific topics have famously been key harbingers of the new frontiers of science, yet, large-scale analyses of their genesis and impact are rare. We investigated one possible factor connected with a topic’s extraordinary growth: scientific prizes. Our longitudinal analysis of nearly all recognized prizes worldwide and over 11,000 scientific topics from 19 disciplines indicates that topics associated with a scientific prize experience extraordinary growth in productivity, impact, and new entrants. Relative to matched non-prizewinning topics, prizewinning topics produce 40% more papers and 33% more citations, retain 55% more scientists, and gain 37 and 47% more new entrants and star scientists, respectively, in the first five-to-ten years after the prize. Funding do not account for a prizewinning topic’s growth. Rather, growth is positively related to the degree to which the prize is discipline-specific, conferred for recent research, or has prize money. These findings reveal new dynamics behind scientific innovation and investment., Scientific revolutions have famously inspired scientists and innovation but large-scale analyses of scientific revolutions in modern science are rare. Here, the authors investigate one possible factor connected with a topic’s extraordinary growth—scientific prizes.

The ability to objectively assess academic performance is critical to rewarding academic merit, charting academic policy, and promoting science. Quintessential to performing these functions is first the ability to collect valid and current data through increasingly automated online interfaces. Moreover, it is crucial to remove disciplinary and other biases from these data, presenting them in ways that support insightful analysis at various levels. Existing systems are lacking in some of these respects. Here we present Scholar Plot (SP), an interface that harvests bibliographic and research funding data from online sources. SP addresses systematic biases in the collected data through nominal and normalized metrics. Eventually, SP combines synergistically these metrics in a plot form for expert appraisal, and an iconic form for broader consumption. SP’s plot and iconic forms are scalable, representing equally well individual scholars and their academic units, thus contributing to consistent ranking practices across the university organizational structure. In order to appreciate the design principles underlying SP, in particular the informativeness of nominal versus normalized metrics, we also present the results of an evaluation survey taken by senior faculty (n=28) with significant promotion and tenure assessment experience.

This Research Letter examines differences in the size of National Institutes of Health (NIH) grants awarded to first-time male and female principal investigators at top research institutions, including the Big Ten and Ivy League universities.

Social influence is typically studied after a product is released. Yet, audience expectations and discussions begin before a product’s release. This observation suggests a need to understand adoption processes over a product’s life cycle. To explore pre- and postrelease social influence processes, this article uses survey data from Americans exposed to word of mouth for 309 Hollywood movies released over two and a half years. The data suggest pre- and postrelease social influences operate differently. Prerelease social influence displays a critical transition point with relation to adoption: before a critical value, any level of social influence is negligibly related to adoption, but after the critical value, the relationship between social influence and adoption is large and substantive. In contrast, postrelease social influence exhibits a positive linear relationship with adoption. Prerelease social influence is argued to require more exposures than postrelease social influence because of differences in the diagnosticity and accessibility of the information. To complement the survey data, computational models are used to test alternative hypotheses. Evidence from the computational models supports the proposed model of social influence.

Scientific prizes are among the greatest recognition a scientist receives from their peers and arguably shape the direction of a field by conferring credibility to persons, ideas, and disciplines, providing financial rewards, and promoting rituals that reinforce scientific communities. The proliferation of prizes and links among prizes suggest that the prize network embodies information about scientists and ideas poised to grow in acclaim. Using comprehensive new data on prizes and prizewinners worldwide and across disciplines, we examine the growth dynamics and interlocking relationships found in the worldwide scientific prize network. We focus on understanding how the knowledge linkages among prizes and scientists' propensities for prizewinning are related to knowledge pathways across disciplines and stratification within disciplines. We find several key links between prizes and scientific advances. First, despite a proliferation of diverse prizes over time and across the globe, prizes are more concentrated within a relatively small group of scientific elites, and ties within the elites are more clustered, suggesting that a relatively constrained number of ideas and scholars lead science. Second, we find that certain prizes are strongly interlocked within and between disciplines by scientists who win multiple prizes, revealing the key pathways by which knowledge systematically gains credit and spreads through the network. Third, we find that genealogical and co authorship networks strongly predict who wins one or more prizes and explains the high level of interconnections among acclaimed scientists and their path breaking ideas.

Peer-to-peer lending is hypothesized to help equalize economic opportunities for the world's poor. We empirically investigate the "flat-world" hypothesis, the idea that globalization eventually leads to economic equality, using crowdfinancing data for over 660,000 loans in 220 nations and territories made between 2005 and 2013. Contrary to the flat-world hypothesis, we find that peer-to-peer lending networks are moving away from flatness. Furthermore, decreasing flatness is strongly associated with multiple variables: relatively stable patterns in the difference in the per capita GDP between borrowing and lending nations, ongoing migration flows from borrowing to lending nations worldwide, and the existence of a tie as a historic colonial. Our regression analysis also indicates a spatial preference in lending for geographically proximal borrowers. To estimate the robustness for these patterns for future changes, we construct a network of borrower and lending nations based on the observed data. Then, to perturb the network, we stochastically simulate policy and event shocks (e.g., erecting walls) or regulatory shocks (e.g., Brexit). The simulations project a drift towards rather than away from flatness. However, levels of flatness persist only for randomly distributed shocks. By contrast, loss of the top borrowing nations produces more flatness, not less, indicating how the welfare of the overall system is tied to a few distinctive and critical country-pair relationships.

Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social network's structure and communicative properties. Here, we study how external events are associated with a network's change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness. Rather, the network "turtles up". It displays a propensity for higher clustering, strong tie interaction, and an intensification of insider vs. outsider communication. Further, we find changes in network structure predict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network., 12 pages, 8 figures, Proceedings of the 25th ACM International World Wide Web Conference (WWW) 2016

Users online tend to select information that support and adhere their beliefs, and to form polarized groups sharing the same view - e.g. echo chambers. Algorithms for content promotion may favour this phenomenon, by accounting for users preferences and thus limiting the exposure to unsolicited contents. To shade light on this question, we perform a comparative study on how same contents (videos) are consumed on different online social media - i.e. Facebook and YouTube - over a sample of 12M of users. Our findings show that content drives the emergence of echo chambers on both platforms. Moreover, we show that the users' commenting patterns are accurate predictors for the formation of echo-chambers.

Scientific articles are retracted at increasing rates, with the highest rates among top journals. Here we show that a single retraction triggers citation losses through an author's prior body of work. Compared to closely-matched control papers, citations fall by an average of 6.9% per year for each prior publication. These chain reactions are sustained on authors' papers (a) published up to a decade earlier and (b) connected within the authors' own citation network by up to 4 degrees of separation from the retracted publication. Importantly, however, citation losses among prior work disappear when authors self-report the error. Our analyses and results span the range of scientific disciplines.

Nodes in cooperative networks, such as those between plants and their pollinators or service providers and their contractors, form complex networks of interdependences. In these mutualistic networks, nodes that contribute to the nestedness of the network improve its stability. However, this study, using ecological data from 20 plant–pollinator networks and from socioeconomic networks, shows that these same nodes do not reap the benefits. In fact, the nodes that contribute the most to network persistence are also the most vulnerable to extinction. The architecture of mutualistic networks facilitates coexistence of individual participants by minimizing competition relative to facilitation1,2. However, it is not known whether this benefit is received by each participant node in proportion to its overall contribution to network persistence. This issue is critical to understanding the trade-offs faced by individual nodes in a network3,4,5. We address this question by applying a suite of structural and dynamic methods to an ensemble of flowering plant/insect pollinator networks. Here we report two main results. First, nodes contribute heterogeneously to the overall nested architecture of the network. From simulations, we confirm that the removal of a strong contributor tends to decrease overall network persistence more than the removal of a weak contributor. Second, strong contributors to collective persistence do not gain individual survival benefits but are in fact the nodes most vulnerable to extinction. We explore the generality of these results to other cooperative networks by analysing a 15-year time series of the interactions between designer and contractor firms in the New York City garment industry. As with the ecological networks, a firm's survival probability decreases as its individual nestedness contribution increases. Our results, therefore, introduce a new paradox into the study of the persistence of cooperative networks, and potentially address questions about the impact of invasive species in ecological systems and new competitors in economic systems.

Digital communication data has created opportunities to advance the knowledge of human dynamics in many areas, including national security, behavioral health, and consumerism. While digital data uniquely captures the totality of a person's communication, past research consistently shows that a subset of contacts makes up a person's “social network” of unique resource providers. To address this gap, we analyzed the correspondence between self-reported social network data and email communication data with the objective of identifying the dynamics in e-communication that correlate with a person's perception of a significant network tie. First, we examined the predictive utility of three popular methods to derive social network data from email data based on volume and reciprocity of bilateral email exchanges. Second, we observed differences in the response dynamics along self-reported ties, allowing us to introduce and test a new method that incorporates time-resolved exchange data. Using a range of robustness checks for measurement and misreporting errors in self-report and email data, we find that the methods have similar predictive utility. Although e-communication has lowered communication costs with large numbers of persons, and potentially extended our number of, and reach to contacts, our case results suggest that underlying behavioral patterns indicative of friendship or professional contacts continue to operate in a classical fashion in email interactions.

Tracking the volume of keywords in Internet searches, message boards, or Tweets has provided an alternative for following or predicting associations between popular interest or disease incidences. Here, we extend that research by examining the role of e-communications among day traders and their collective understanding of the market. Our study introduces a general method that focuses on bundles of words that behave differently from daily communication routines, and uses original data covering the content of instant messages among all day traders at a trading firm over a 40-month period. Analyses show that two word bundles convey traders' understanding of same day market events and potential next day market events. We find that when market volatility is high, traders' communications are dominated by same day events, and when volatility is low, communications are dominated by next day events. We show that the stronger the traders' attention to either same day or next day events, the higher their collective trading performance. We conclude that e-communication among traders is a product of mass collaboration over diverse viewpoints that embodies unique information about their weak or strong understanding of the market.