主 题: Scalable Spectral Algorithms for Community Detection in Directed Networks
报告人: Prof. Tao Shi (The Ohio State University)
时 间: 2013-03-22 14:00-15:00
地 点: 理科一号楼1114 （数学所活动）
Community detection has been one of the central problems in network
studies and directed network is particular challenging due to asymmetry
among its links. In this talk, we discuss incorporating the direction of
links reveals new perspective on communities regarding to two different
roles, source and terminal. Intriguingly, such communities appear to be
connected with unique spectral property of the graph Laplacian of the
adjacency matrix and we exploit this connection by using regularized SVD
methods. We propose harvesting algorithms, coupled with regularized SVDs,
that are linearly scalable for efficient identification of communities in
huge directed networks.The algorithm showed great performance and
scalability on benchmark networks in simulations and successfully
recovered communities in real social networks applications (with ～ 2
million nodes and ～ 50 million edges). This is a joint work with Sungmin
Kim (OSU)。
