Hyperedge Importance Estimation via Identity-aware Hypergraph Attention Network
Yin Chen, Xiaoyang Wang, Chen Chen · 2024
Hypergraphs provide a more flexible representation for group interactions in complex systems compared to ordinary graphs, where each hyperedge can connect any number of nodes. In practice, data modeled as hypergraphs often contain hyperedge importance values, which indicate the influence or popularity of the group collaborations. For example, in a co-authorship hypergraph, a paper (hyperedge) is co-authored by multiple authors (nodes). The number of citations a paper receives can be regarded as the importance value of its corresponding hyperedge, reflecting its academic influence and significance.