A0219
Title: Community detection in general hypergraph via garph embedding
Authors: Yaoming Zhen - City University of Hong Kong (Hong Kong) [presenting]
Junhui Wang - Chinese University of Hong Kong (Hong Kong)
Abstract: Conventional network data has primarily focused on pairwise interactions, yet multi-way interactions among multiple entities have been frequently observed in real-life hypergraph networks. A novel method is proposed for detecting community structure in general hypergraph networks, uniform or non-uniform. The proposed approach introduces a null vertex to augment a non-uniform hypergraph into a uniform multi-hypergraph and then embeds the multi-hypergraph in a low-dimensional vector space such that vertices within the same community are close to each other. The resultant optimization task can be efficiently tackled by an alternative updating scheme. The asymptotic consistencies of the proposed method are established in terms of both community detection and hypergraph estimation, which are also supported by numerical experiments on some synthetic and real-life hypergraph networks.
