Tools for higher-order network analysis
Networks are a fundamental model of complex systems in biology, neuroscience, engineering, and social science. Networks are typically described by lower-order connectivity patterns that are captured at the level of individual nodes and edges. However, higher-order connectivity patterns captured by small subgraphs, or network motifs, describe the fundamental structures that control and mediate the behavior of many complex systems. In this talk, I will discuss several higher-order analyses based on higher-order connectivity patterns that I have developed to gain new insights into network data. Specifically, I will introduce a motif-based clustering methodology, a formalism for temporal motifs to study temporal networks, and a new stochastic process for tensor data. I will also show applications of higher-order analysis in several domains including ecology, biology, transportation, and human communication.
Austin Benson is a PhD candidate at Stanford University in the Institute for Computational and Mathematical Engineering where he is advised by Professor Jure Leskovec of the Computer Science Department. His research focuses on data-driven methods for understanding complex systems and behavior, which spans network science, matrix computations, and modeling human behavior on the Web. Before Stanford, he completed undergraduate degrees in Computer Science and Applied Mathematics at the University of California, Berkeley. Outside of the university, he has spent summers interning at Google (four times), Sandia National Laboratories, and HP Labs.