Graph Ricci Flow and Applications in Network Analysis and Learning - Jie Gao скачать в хорошем качестве

Graph Ricci Flow and Applications in Network Analysis and Learning - Jie Gao

Abstract: The notion of curvature describes how spaces are bent at each point and Ricci flow deforms the space such that curvature changes in a way analogous to the diffusion of heat. In this talk I will discuss some of our work on discrete Ollivier Ricci curvature defined on graphs. Discrete curvature defined on an edge captures the local connectivity in the neighborhood. In general edges within a densely connected community have positive curvature while edges connecting different communities have negative curvature. By deforming edge weights with respect to curvature one can derive a Ricci flow metric which is robust to edge insertion/deletion. I will present applications of graph Ricci flow in graph analysis and learning, including network alignment, community detection and graph neural networks. Biography: Jie Gao is a Professor of Computer Science department of Rutgers University. From 2005-2019 she was on faculty of Department of Computer Science, Stony Brook University. Her reserach is in the intersection of Algorithm Design, Computational Geometry and Networking applications such as wireless, mobile, and sensor networks, and more recently social networks, trajectory data/privacy, and scheduling problems in robotics and networking.