| Speaker: | Dong XIA, HKUST, Hong Kong SAR |
| Host: | Yanxi Hou, School of Data Science, Fudan University |
| Time: | 15:00-16:00, December 12, 2019 |
| Location: | Zibin N102, Fudan University |
| Abstract: | To date, social network analysis has been largely focused on pairwise interactions. The study of higher-order interactions, via a hypergraph network, brings in new insights. We study community detection in a hypergraph network. A popular approach is to project the hypergraph to a graph and then apply community detection methods for graph networks, but we show that this approach may cause unwanted information loss. We propose a new method for community detection that operates directly on the hypergraph. At the heart of our method is a regularized higher-order orthogonal iteration (reg-HOOI) algorithm that computes an approximate low-rank decomposition of the network adjacency tensor. Compared with existing tensor decomposition methods such as HOSVD and vanilla HOOI, reg-HOOI yields better performance, especially when the hypergraph is sparse. Given the output of tensor decomposition, we then generalize the community detection method SCORE (Jin, 2015) from graph networks to hypergraph networks. This talk is based on a joint work with Zheng Tracy Ke and Feng Bill Shi. |
| Bio: | Dr Dong XIA is an Assistant Professor in the Department of Mathematics, Hong Kong University of Science and Technology,Hong Kong SAR. |