Modularity Definition and Optimization Algorithm for Community Detection in Signed Hypergraphs

The analysis of super-dyadic relations through hypergraphs is gradually gaining attention, with its community structure analysis playing a crucial role in computational social science. However, few scholars have paid attention to the impact of hyperedge diversity on the community structure of hypergraphs, especially the impact generated by heterogeneous hyperedges. This paper expands hypergraphs into signed hypergraphs and proposes a framework for community structure in signed hypergraphs along with a variant of modularity. Simultaneously, an optimization algorithm is introduced in this paper to detect potential communities by maximizing modularity. Experimental results reveal that the proposed method can effectively optimize the objective function and detect community structures.