Community Detection in General Hypergraph Via Graph Embedding

Network data has attracted tremendous attention in recent years, and most conventional networks focus on pairwise interactions between two vertices. However, real-life network may display more complex structures, multi-way among vertices arise naturally. In this article, we propose a novel method for detecting community structure general hypergraph networks, uniform or non-uniform. The proposed introduces null vertex to augment non-uniform into multi-hypergraph, then embeds the multi-hypergraph low-dimensional vector space such that within same are close each other. resultant optimization task can be efficiently tackled by an alternative updating scheme. asymptotic consistencies of established terms both detection estimation, which also supported numerical experiments some synthetic networks.