III: Small: MESH: A Hypergraph Analysis Engine for Understanding Large-Scale Social Networks

University Of Minnesota-Twin Cities, Minneapolis MN

Investigators

Abstract

Rapid growth in the amount and richness of online interactions through social networking applications is creating data at unprecedented scales. This includes data about individual's characteristics as well as their connections and interactions. Many real-world applications have complex group dynamics involving multiple people. The analysis of such group interactions has the potential to revolutionize social sciences, business, and commerce domains. The goal of this project is to develop a novel computational framework to support scalable analysis of group dynamics in large social networks. The key idea is to explicitly model groups of individuals rather than simply capturing links between pairs of individuals. The broader impacts of this project will consist of enabling richer analysis of complex interactions in real-world networks. It will also enhance the University of Minnesota Computer Science curriculum through courses and research experiences with synergy between the areas of computer systems and data mining. To model group interactions in networks, this project will use hypergraphs, a generalization of graphs, where hyperedges represent relations between one or more entities. Hypergraphs have the potential to provide higher modeling accuracy for many group phenomena, as well as higher storage and computational efficiency, compared to their graph counterparts. This project will develop an analysis framework called MESH: Minnesota Engine for Scalable evolving Hypergraph analysis, that will provide algorithms and system components to support scalable analysis of evolving hypergraphs. The MESH algorithms will be designed to model and compute several common data-driven questions related to group dynamics in real-world networks. The MESH system-level techniques will be designed to support the execution of these algorithms in a distributed and scalable manner. For further information see the project web site at: http://mesh.cs.umn.edu