arXiv subjects

Brian Uzzi

Publications and source records attributed to Brian Uzzi.

At least 19 recordsLinked to original sources

arXiv2026

Artificial Intelligence in Science: Returns, Reallocation, and Reorganization

Investment in artificial intelligence (AI) has grown rapidly, yet its returns to scientific research remain poorly understood. We study how AI reshapes the production of science using a comprehensive dataset of research proposals submitted to a large international funding agency, including both funded and unfunded projects. Combining keyword extraction with large language model classification, we identify the presence, type, and functional role of AI within each proposal and link these measures to detailed budget allocations, team structure, and subsequent publication outcomes. We find that, in the short run, AI adoption is associated with modest improvements in scientific outcomes concentrated in the upper tail. Instead, its primary effects arise in the organization of research: AI-enabled projects reallocate resources toward human capital, involve larger teams, and undertake a broader set of tasks. These patterns are consistent with a reorganization of the scientific production process rather than immediate efficiency gains, in line with theories of general-purpose technologies. Task-level analyses further show that activities expanded in AI-enabled projects, particularly ideation and experimentation, are increasingly compatible with large language model capabilities, suggesting potential for future productivity gains as these technologies mature.

physics.soc-ph↗

arXiv2025

Analysis of Collaboration in CS Prizewinning with a Nobel-Turing Comparison

In the scientific community, prizes play a pivotal role in shaping research trajectories by conferring credibility and offering financial incentives to researchers. Yet, we know little about the relationship between academic collaborations and prizewinning. By analyzing over 100 scientific prizes and the collaboration behaviors of over 5,000 prizewinners in CS, we find that prizewinners collaborate earlier and more frequently with other prizewinners than researchers who have not yet received similar recognition. Moreover, CS researchers across age groups collaborate more with prizewinners after winning their first prize, and collaborating with prizewinners after their first win increases the likelihood of the collaborator winning an award. We find that recipients of general CS prizes collaborate more than recipients of more specialized prizes, who collaborate less frequently. With Coarsened Exact Matching (CEM) and regression, we find an increase in prizewinning odds with strength of prizewinner collaboration. We examine the context of recent Nobel Prizes going to CS researchers by showing how an increasing share of Physics awards go to Physics-CS collaborations, and contrast Nobel-Turing winning author's trajectories. Our findings shed light on the relationship between prizewinning and collaboration.

cs.SI↗

arXiv2025

A High-Scale Assessment of Social Media and Mainstream Media in Scientific Communication

Communication of scientific knowledge beyond the walls of science is key to science's societal impact. Media channels play sizable roles in disseminating new scientific ideas about human health, economic welfare, and government policy as well as responses to emergent challenges such as climate change. Indeed, effectively communicating science to the public helps inform society's decisions on scientific and technological policies, the value of science, and investment in research. At the same time, the rise of social media has greatly changed communication systems, which may substantially affect the public's interface with science. Examining 20.9 million scientific publications, we compare research coverage in social media and mainstream media in a broad corpus of scientific work. We find substantial shifts in the scale, impact, and heterogeneity of scientific coverage. First, social media significantly alters what science is, and is not, covered. Whereas mainstream media accentuates eminence in the coverage of science and focuses on specific fields, social media more evenly sample research according to field, institutional rank, journal, and demography, increasing the scale of scientific ideas covered relative to mainstream outlets more than eightfold. Second, despite concerns about the quality of science represented in social media, we find that social media typically covers scientific works that are impactful and novel within science. Third, scientists on social media, as experts in their domains, tend to surface high-impact research in their own fields while sampling widely across research institutions. Contrary to prevalent observations about social media, these findings reveal that social media expands and diversifies science reporting by highlighting high-impact research and bringing a broader array of scholars, institutions and scientific concepts into public view.

physics.soc-ph↗

arXiv2025

Peer Review and the Diffusion of Ideas

This study examines a fundamental yet overlooked function of peer review: its role in exposing reviewers to new and unexpected ideas. Leveraging a natural experiment involving over half a million peer review invitations covering both accepted and rejected manuscripts, and integrating high-scale bibliographic and editorial records for 37,279 submitting authors, we find that exposure to a manuscript's core ideas significantly influences the future referencing behavior and knowledge of reviewer invitees who decline the review invite. Specifically, declining reviewer invitees who could view concise summaries of the manuscript's core ideas not only increase their citations to the manuscript itself but also demonstrate expanded breadth, depth, diversity, and prominence of citations to the submitting author's broader body of work. Overall, these results suggest peer review substantially influences the spread of scientific knowledge. Ironically, while the massive scale of peer review, entailing millions of reviews annually, often drives policy debates about its costs and burdens, our findings demonstrate that precisely because of this scale, peer review serves as a powerful yet previously unrecognized engine for idea diffusion, which is central to scientific advances and scholarly communication.

physics.soc-ph↗

arXiv2024

Quantifying the Dynamics of Innovation Abandonment Across Scientific, Technological, Commercial, and Pharmacological Domains

Despite the vast literature on the diffusion of innovations that impacts a broad range of disciplines, our understanding of the abandonment of innovations remains limited yet is essential for a deeper understanding of the innovation lifecycle. Here, we analyze four large-scale datasets that capture the temporal and structural patterns of innovation abandonment across scientific, technological, commercial, and pharmacological domains. The paper makes three primary contributions. First, across these diverse domains, we uncover one simple pattern of preferential abandonment, whereby the probability for individuals or organizations to abandon an innovation increases with time and correlates with the number of network neighbors who have abandoned the innovation. Second, we find that the presence of preferential abandonment fundamentally alters the way in which the underlying ecosystem breaks down, inducing a novel structural collapse in networked systems commonly perceived as robust against abandonments. Third, we derive an analytical framework to systematically understand the impact of preferential abandonment on network dynamics, pinpointing specific conditions where it may accelerate, decelerate, or have an identical effect compared to random abandonment, depending on the network topology. Together, these results deepen our quantitative understanding of the abandonment of innovation within networked social systems, with implications for the robustness and functioning of innovation communities. Overall, they demonstrate that the dynamics of innovation abandonment follow simple yet reproducible patterns, suggesting that the uncovered preferential abandonment may be a generic property of the innovation lifecycle.

physics.soc-ph↗

arXiv2024

The Innovative Distinctiveness of Prizewinners and their Networks

Science prizes purportedly reward innovation and explorations of new phenomena. Yet, in practice prizes may inadvertently divert resources from similarly impactful but less celebrated scholars. Despite this paradox, knowledge of how prizewinning relates to innovation is nascent even as prizes proliferate widely. Analyzing 2,460 worldwide prizes, we compared the innovativeness of over 23,000 prizewinners and matched non-prizewinners whose performance records were statistically equivalent up to the prize year. First, we find that prizewinners are more innovative. Their research is more likely to combine existing ideas in new ways, integrate a topic's historical and contemporary thinking, and incorporate interdisciplinary perspectives. Second, although prizewinners and matched non-prizewinners have statistically equivalent impact and productivity records up to the prize year, at about five years before the prize, prizewinners' papers become more innovative than their matched peers, a difference that widens each year, peaks during the prize year, and then persists for the remainder of their careers. Third, network embeddedness predicts unusual innovativeness. Compared to non-prizewinners, prizewinners' collaborations are shorter in duration, encompass wider exposure to unfamiliar topics, and involve coauthors whose networks minimally overlap with each other. The implications of the findings for the efficacy of reward systems and innovation in science are discussed.

cs.DL↗

arXiv2024

High-Impact Innovations and Hidden Gender Disparities in Inventor-Evaluator Networks

We study of millions of scientific, technological, and artistic innovations and find that the innovation gap faced by women is far from universal. No gap exists for conventional innovations. Rather, the gap is pervasively rooted in innovations that combine ideas in unexpected ways - innovations most critical to scientific breakthroughs. Further, at the USPTO we find that female examiners reject up to 33 percent more unconventional innovations by women inventors than do male examiners, suggesting that gender discrimination weakly explains this innovation gap. Instead, new data indicate that a configuration of institutional practices explains the innovation gap. These practices compromise the expertise women examiners need to accurately assess unconventional innovations and then "over-assign" women examiners to women innovators, undermining women's innovations. These institutional impediments negatively impact innovation rates in science but have the virtue of being more amenable to actionable policy changes than does culturally ingrained gender discrimination.

cs.SI↗

arXiv2024

Promotional Language and the Adoption of Innovative Ideas in Science

How are the merits of innovative ideas communicated in science? Here we conduct semantic analyses of grant application success with a focus on scientific promotional language, which has been growing in frequency in many contexts and purportedly may convey an innovative idea's originality and significance. Our analysis attempts to surmount limitations of prior studies by examining the full text of tens of thousands of both funded and unfunded grants from three leading public and private funding agencies: the NIH, the NSF, and the Novo Nordisk Foundation, one of the world's largest private science foundations. We find a robust association between promotional language and the support and adoption of innovative ideas by funders and other scientists. First, the percentage of promotional language in a grant proposal is associated with up to a doubling of the grant's probability of being funded. Second, a grant's promotional language reflects its intrinsic level of innovativeness. Third, the percentage of promotional language predicts the expected citation and productivity impact of publications that are supported by funded grants. Lastly, a computer-assisted experiment that manipulates the promotional language in our data demonstrates how promotional language can communicate the merit of ideas through cognitive activation. With the incidence of promotional language in science steeply rising, and the pivotal role of grants in converting promising and aspirational ideas into solutions, our analysis provides empirical evidence that promotional language is associated with effectively communicating the merits of innovative scientific ideas.

cs.DL↗

arXiv2022

An Exploratory Study of Stock Price Movements from Earnings Calls

Financial market analysis has focused primarily on extracting signals from accounting, stock price, and other numerical hard data reported in P&L statements or earnings per share reports. Yet, it is well-known that the decision-makers routinely use soft text-based documents that interpret the hard data they narrate. Recent advances in computational methods for analyzing unstructured and soft text-based data at scale offer possibilities for understanding financial market behavior that could improve investments and market equity. A critical and ubiquitous form of soft data are earnings calls. Earnings calls are periodic (often quarterly) statements usually by CEOs who attempt to influence investors' expectations of a company's past and future performance. Here, we study the statistical relationship between earnings calls, company sales, stock performance, and analysts' recommendations. Our study covers a decade of observations with approximately 100,000 transcripts of earnings calls from 6,300 public companies from January 2010 to December 2019. In this study, we report three novel findings. First, the buy, sell and hold recommendations from professional analysts made prior to the earnings have low correlation with stock price movements after the earnings call. Second, using our graph neural network based method that processes the semantic features of earnings calls, we reliably and accurately predict stock price movements in five major areas of the economy. Third, the semantic features of transcripts are more predictive of stock price movements than sales and earnings per share, i.e., traditional hard data in most of the cases.

q-fin.ST↗

arXiv2021

Event Detection on Dynamic Graphs

Event detection is a critical task for timely decision-making in graph analytics applications. Despite the recent progress towards deep learning on graphs, event detection on dynamic graphs presents particular challenges to existing architectures. Real-life events are often associated with sudden deviations of the normal behavior of the graph. However, existing approaches for dynamic node embedding are unable to capture the graph-level dynamics related to events. In this paper, we propose DyGED, a simple yet novel deep learning model for event detection on dynamic graphs. DyGED learns correlations between the graph macro dynamics -- i.e. a sequence of graph-level representations -- and labeled events. Moreover, our approach combines structural and temporal self-attention mechanisms to account for application-specific node and time importances effectively. Our experimental evaluation, using a representative set of datasets, demonstrates that DyGED outperforms competing solutions in terms of event detection accuracy by up to 8.5% while being more scalable than the top alternatives. We also present case studies illustrating key features of our model.

cs.LG↗

arXiv2021

Meta-Learning with Graph Neural Networks: Methods and Applications

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different meta-learning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

cs.LG↗

arXiv2020

Scientific Prizes and the Extraordinary Growth of Scientific Topics

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 investigate 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.

cs.DL↗

arXiv2020

The rising of collective forgetting and cultural selectivity in inventors and physicists communities

How long until this paper is forgotten? Collective forgetting is the process by which the attention received by cultural pieces decays as time passes. Recent work modeled this decay as the result of two different processes, one linked to communicative memory --memories sustained by human communication-- and cultural memory --memories sustained by the physical recording of content. Yet, little is known on how the collective forgetting dynamic changes over time. Are older cultural pieces forgotten at a lower rate than newer ones? Here, we study the temporal changes of collective memory and attention by focusing on two knowledge communities: inventors and physicists. We use data on patents from the United States Patent and Trademark Office (USPTO) and physics papers published in the American Physical Society (APS) to quantify how collective forgetting has changed over time. The model enables us to distinguish between two branches of forgetting. One branch is short-lived, going directly from communicative memory to oblivion. The other one is long-lived going from communicative to cultural memory and then to oblivion. The data analysis shows an increasing forgetting rate for both communities as the information grows. Furthermore, these knowledge communities seem to be increasing their selectivity at storing valuable cultural pieces in their cultural memory. These findings provide empirical confirmation on the forgetting as an annulment hypothesis and show that knowledge communities can effectively slow down the rising of collective forgetting at improving their cultural selectivity.

physics.soc-ph↗

arXiv2018

The Scientific Prize Network Predicts Who Pushes the Boundaries of Science

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.

physics.soc-ph↗

arXiv2018

A network-based citation indicator of scientific performance

Scientists are embedded in social and information networks that influence and are influenced by the quality of their scientific work, its impact, and the recognition they receive. Here we quantify the systematic relationship between a scientist's position in the network of scientific collaborations and the citations they receive. As expected, we find that authors closer to others in this network are, on average, more highly cited than those further away from others. We construct a novel indicator, the s-index, that explicitly captures performance linked to network position along two complimentary dimensions: performance expected due to network position and performance relative to this position. The basis of our approach is to represent an author's network position through their distribution of distances to other authors. The s-index then ranks (1) the citation potential of an individual's network position relative to all other authors, and (2) the citations they accrue relative to authors that have a comparable network position. Characterizing scientists through these two complimentary dimensions can be used to make more informed evaluations in a networked environment. For example, it can identify individuals that play an important role in diffusing scientific ideas. It also sheds a new light on central debates in the Science of Science, namely the impact of author teams and comparisons of impact across scientific fields.

cs.DL↗

arXiv2016

The Formation and Imprinting of Network Effects Among the Business Elite

The business elite constitutes a small but strikingly influential subset of the population, oftentimes affecting important societal outcomes such as the consolidation of political power, the adoption of corporate governance practices, and the stability of national economies more broadly. Here we analyze a unique dataset of all MBA students at a top 5 MBA program. After matching students on all available characteristics (e.g., age, grade scores, industry experience, etc.) - i.e. creating twin pairs - we find that the distinguishing characteristics between students who do well in job placement and those who do not is their network. Further, we find that the network differences between the successful and unsuccessful students develops within the first month of class and persists thereafter, suggesting a network imprinting that is persistent. Finally, we find that these effects are pronounced for students who are at the extreme ends of the distribution on other measures of success - students with the best expected job placement do particularly poorly without the right network (descenders), whereas students with worst expected job placement pull themselves to the top of the placement hierarchy (ascenders) with the right network.

physics.soc-ph↗

arXiv2016

Users Polarization on Facebook and Youtube

On social media algorithms for content promotion, accounting for users preferences, might limit the exposure to unsolicited contents. In this work, we study how the same contents (videos) are consumed on different platforms -- i.e. Facebook and YouTube -- over a sample of $12M$ of users. Our findings show that the same content lead to the formation of echo chambers, irrespective of the online social network and thus of the algorithm for content promotion. Finally, we show that the users' commenting patterns are accurate early predictors for the formation of echo-chambers.

cs.SI↗

arXiv2016

Social Networks Under Stress

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.

cs.SI↗