Hypernetwork Representation Learning Based on Hyperedge Modeling

Abstract

Most network representation learning approaches only consider the pairwise relationships between the nodes in ordinary networks but do not consider the tuple relationships, namely the hyperedges, among the nodes in the hypernetworks. Therefore, to solve the above issue, a hypernetwork representation learning approach based on hyperedge modeling, abbreviated as HRHM, is proposed, which fully considers the hyperedges to obtain ideal node representation vectors that are applied to downstream machine learning tasks such as node classification, link prediction, community detection, and so on. Experimental results on the hypernetwork datasets show that with regard to the node classification task, the mean node classification accuracy of HRHM approach goes beyond other best baseline approach by about 1% on the MovieLens and wordnet, and with regard to the link prediction task, except for HPHG approach, the mean AUC value of HRHM approach surpasses that of other baseline approaches by about 17%, 18%, and 6%, respectively, on the GPS, drug, and wordnet. The mean AUC value of HRHM approach is very close to that of other best baseline approach on the MovieLens.