Research Seminar: "Graph Ricci Flow and Applications" by Prof. Jie Gao скачать в хорошем качестве

Research Seminar: "Graph Ricci Flow and Applications" by Prof. Jie Gao

Spring 2021 SIP Seminar Series: April 7, 2021 [http://www.inspirelab.us/seminars/] Speaker: Prof. 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 recent work in my group 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 show applications of graph Ricci flow in graph analysis and learning, including network alignment, community detection and graph neural networks. Biography: Professor Jie Gao is currently Professor in computer science, Rutgers University. She was on the faculty of Computer Science department at Stony Brook University from 2005-2019. She received B.Eng from the Special Class of the Gifted Young, University of Science and Technology of China in 1999, Ph.D in Computer Science from Stanford University in 2004 and a postdoc at Caltech from 2004-2005. She received the NSF career award in 2006, IMC best paper award and multiple Research Excellence Award in computer science department of Stony Brook. She is currently serving on the editorial board of ACM Transactions on Sensor Networks and International Journal of Computational Geometry and Applications. She published over 140 referred papers in computer networking and theoretical computer science fields, and has graduated over 16 Ph.D students.