CAREER: Mining structure and dynamics of groups of nodes in real-world networks

Stanford University, Stanford CA

Investigators

Abstract

Social, technological, information and biological systems can be studied as graphs, where nodes represent entities (i.e., people, websites) and edges represent interactions (friendships, communication). The project aims to analyze and discover explanatory and predictive models of networked systems, such as large groups of people and societies, or large biological and technological systems, in order to understand their structure and make predictions about their global dynamics. The research studies the structure and dynamics of communities of nodes, with the goal to invent novel network community detection methods and build predictive models of behavior of groups of nodes. The proposed research has three main thrusts: (1) Structure and discovery of network communities, (2) Dynamics and "health" of network communities, and (3) Supervised community detection in networks with rich node and edge metadata. The research focuses on harnessing massive network datasets, as certain behaviors and patterns are observable only when the amount of data is large enough. The intellectual focus of the project is on increasing the expressivity of the models to also include rich node and edge metadata and explore the connections between the network structure and the attributes/features of nodes and edges. The education plan provides for rich research experiences and helps students develop the interdisciplinary attitudes and skills needed for this work through courses that look at real-world network problems and data. An integral part of this proposal is public release of datasets and computational tools for analysis of large networks. Additional information about the project including publications, data sets, source code, and educational materials can be accessed through the project website at http://snap.stanford.edu.