Network data has attracted tremendous attention in recent years, and most conventional networks focus on pairwise interactions between two vertices. However, real-life network data may display more complex structures, and multi-way interactions among vertices arise naturally, leading to hypergraph networks. In this talk, we will present a novel method for detecting community structure in general hypergraph networks, uniform or non-uniform. It first 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 asymptotic properties of the proposed method will be discussed in terms of both community detection and hypergraph estimation, which are also supported by numerical experiments on some simulated and real examples.