Generative growing hypergraph leaning

The majority of existing studies on dynamic hypergraphs focus on hypergraphs with a constant size but only dynamic hyperedges, yet numerous scenarios necessitate the understanding of a hypergraph's growth. This paper introduces the Variational Growing Hypergraph Learning (VGHL) method, which addresses the limitations of current studies that only consider hypergraphs with fixed sizes and dynamic hyperedges. The VGHL method is designed to simultaneously capture the evolving structure of an existing hypergraph and accommodate the integration of new nodes. The technique involves transforming hypergraph snapshots into line graphs and then adjusting the variational lower bound to facilitate the construction of a hypergraph sequence, which is crucial for downstream classification tasks. The paper demonstrates the efficacy of the VGHL method through experiments on various benchmark datasets, highlighting its potential for semi-supervised classification.