Science has long been viewed as a key driver of economic growth and rising standards of living. Knowledge about how scientific advances support marketplace inventions is therefore essential for understanding the role of science in propelling real-world applications and technological progress. The increasing availability of large-scale datasets tracing scientific publications and patented inventions and the complex interactions among them offers us new opportunities to explore the evolving dual frontiers of science and technology at an unprecedented level of scale and detail. However, we lack suitable visual analytics approaches to analyze such complex interactions effectively. Here we introduce InnovationInsights, an interactive visual analysis system for researchers, research institutions, and policymakers to explore the complex linkages between science and technology, and to identify critical innovations, inventors, and potential partners. The system first identifies important associations between scientific papers and patented inventions through a set of statistical measures introduced by our experts from the field of the Science of Science. A series of visualization views are then used to present these associations in the data context. In particular, we introduce the Interplay Graph to visualize patterns and insights derived from the data, helping users effectively navigate citation relationships between papers and patents. This visualization thereby helps them identify the origins of technical inventions and the impact of scientific research. We evaluate the system through two case studies with experts followed by expert interviews. We further engage a premier research institution to test-run the system, helping its institution leaders to extract new insights for innovation. Through both the case studies and the engagement project, we find that our system not only meets our original goals of design, allowing users to better identify the sources of technical inventions and to understand the broad impact of scientific research; it also goes beyond these purposes to enable an array of new applications for researchers and research institutions, ranging from identifying untapped innovation potential within an institution to forging new collaboration opportunities between science and industry.

Existing equivariant neural networks require prior knowledge of the symmetry group and discretization for continuous groups. We propose to work with Lie algebras (infinitesimal generators) instead of Lie groups. Our model, the Lie algebra convolutional network (L-conv) can automatically discover symmetries and does not require discretization of the group. We show that L-conv can serve as a building block to construct any group equivariant feedforward architecture. Both CNNs and Graph Convolutional Networks can be expressed as L-conv with appropriate groups. We discover direct connections between L-conv and physics: (1) group invariant loss generalizes field theory (2) Euler-Lagrange equation measures the robustness, and (3) equivariance leads to conservation laws and Noether current. These connections open up new avenues for designing more general equivariant networks and applying them to important problems in physical sciences.

Modeling and predicting citation dynamics of individual articles is important due to its critical role in a wide range of decisions in science. While the current modeling framework successfully captures citation dynamics of typical articles, there exists a nonnegligible, and perhaps most interesting, fraction of atypical articles whose citation trajectories do not follow the normal rise‐and‐fall pattern. Here we systematically study and classify citation patterns of atypical articles, finding that they can be characterized by awakened articles, second‐acts, and a combination of both. We propose a second‐act model that can accurately describe the citation dynamics of second‐act articles. The model not only provides a mechanistic framework to understand citation patterns of atypical articles, separating factors that drive impact, but it also offers new capabilities to identify the time of exogenous events that influence citations.

Human creativity is the ultimate driving force behind scientific progress. While the building blocks of innovations are often embodied in existing knowledge, it is creativity that blends seemingly disparate ideas. Existing studies have made striding advances in quantifying creativity of scientific publications by investigating their citation relationships. Yet, little is known hitherto about the underlying mechanisms governing scientific creative processes, largely due to that a paper's references, at best, only partially reflect its authors' actual information consumption. This work represents an initial step towards fine-grained understanding of creative processes in scientific enterprise. In specific, using two web-scale longitudinal datasets (120.1 million papers and 53.5 billion web requests spanning 4 years), we directly contrast authors' information consumption behaviors against their knowledge products. We find that, of 59.0% papers across all scientific fields, 25.7% of their creativity can be readily explained by information consumed by their authors. Further, by leveraging these findings, we develop a predictive framework that accurately identifies the most critical knowledge to fostering target scientific innovations. We believe that our framework is of fundamental importance to the study of scientific creativity. It promotes strategies to stimulate and potentially automate creative processes, and provides insights towards more effective designs of information recommendation platforms.

An ability to predict the popularity dynamics of individual items within a complex evolving system has important implications in an array of areas. Here we propose a generative probabilistic framework using a reinforced Poisson process to explicitly model the process through which individual items gain their popularity. This model distinguishes itself from existing models via its capability of modeling the arrival process of popularity and its remarkable power at predicting the popularity of individual items. It possesses the flexibility of applying Bayesian treatment to further improve the predictive power using a conjugate prior. Extensive experiments on a longitudinal citation dataset demonstrate that this model consistently outperforms existing popularity prediction methods.

In many diverse settings, aggregated opinions of others play an increasingly dominant role in shaping individual decision making. One key prerequisite of harnessing the "crowd wisdom" is the independency of individuals' opinions, yet in real settings collective opinions are rarely simple aggregations of independent minds. Recent experimental studies document that disclosing prior collective opinions distorts individuals' decision making as well as their perceptions of quality and value, highlighting a fundamental disconnect from current modeling efforts: How to model social influence and its impact on systems that are constantly evolving? In this paper, we develop a mechanistic framework to model social influence of prior collective opinions (e.g., online product ratings) on subsequent individual decision making. We find our method successfully captures the dynamics of rating growth, helping us separate social influence bias from inherent values. Using large-scale longitudinal customer rating datasets, we demonstrate that our model not only effectively assesses social influence bias, but also accurately predicts long-term cumulative growth of ratings solely based on early rating trajectories. We believe our framework will play an increasingly important role as our understanding of social processes deepens. It promotes strategies to untangle manipulations and social biases and provides insights towards a more reliable and effective design of social platforms.

Our understanding of how individual mobility patterns shape and impact the social network is limited, but is essential for a deeper understanding of network dynamics and evolution. This question is largely unexplored, partly due to the difficulty in obtaining large-scale society-wide data that simultaneously capture the dynamical information on individual movements and social interactions. Here we address this challenge for the first time by tracking the trajectories and communication records of 6 Million mobile phone users. We find that the similarity between two individuals' movements strongly correlates with their proximity in the social network. We further investigate how the predictive power hidden in such correlations can be exploited to address a challenging problem: which new links will develop in a social network. We show that mobility measures alone yield surprising predictive power, comparable to traditional network-based measures. Furthermore, the prediction accuracy can be significantly improved by learning a supervised classifier based on combined mobility and network measures. We believe our findings on the interplay of mobility patterns and social ties offer new perspectives on not only link prediction but also network dynamics.

Information spreading processes are central to human interactions. Despite recent studies in online domains, little is known about factors that could affect the dissemination of a single piece of information. In this paper, we address this challenge by combining two related but distinct datasets, collected from a large scale privacy-preserving distributed social sensor system. We find that the social and organizational context significantly impacts to whom and how fast people forward information. Yet the structures within spreading processes can be well captured by a simple stochastic branching model, indicating surprising independence of context. Our results build the foundation of future predictive models of information flow and provide significant insights towards design of communication platforms.