Heterogeneous Hypernetwork Representation Learning With Hyperedge Fusion

Most of the existing hypernetwork representation learning methods fail to fully consider the hyperedges, leading to the untapped potential of information contained within the hyperedges. To address this issue, this article proposes a heterogeneous hypernetwork representation learning method with hyperedge fusion abbreviated as HRHF. First, this method incorporates the hyperedges into random walk node sequences by means of incidence graph to enhance tuple relationships, i.e., the hyperedges among the nodes. Second, under the condition of the above random walk node sequences, the cognitive structure model, cognitive set model, and cognitive hyperedge model are jointly optimized to comprehensively consider pairwise relationships and tuple relationships among the nodes to learn high-quality node representation vectors. The experimental results demonstrate that, for the link prediction task, this method outperforms other optimal baseline methods, i.e., hyper-path-based random walks + hyper-gram (HPHG) by 0.99% points on the drug dataset, is comparable to the other optimal method, i.e., Event2vec on the global positioning system (GPS) dataset, is close to the performance of other optimal methods, i.e., hyper-path-based random walks + skip-gram (HPSG) on the MovieLens, and is close to the performance of other optimal methods, i.e., Hyper2vec on the WordNet dataset. For the hypernetwork reconstruction task, this method achieves superior average performance on the drug and GPS datasets compared with other baseline methods.