Hypernetwork Representation Learning with the Set Constraint

Abstract

There are lots of situations that cannot be described by traditional networks but can be described perfectly by the hypernetwork in the real world. Different from the traditional network, the hypernetwork structure is more complex and poses a great challenge to existing network representation learning methods. Therefore, in order to overcome the challenge of the hypernetwork structure faced by network representation learning, this paper proposes a hypernetwork representation learning method with the set constraint abbreviated as HRSC, which incorporates the hyperedge set associated with the nodes into the process of hypernetwork representation learning to obtain node representation vectors including the hypernetwork topology structure and hyperedge information. Our proposed method is extensively evaluated by the machine learning tasks on four hypernetwork datasets. Experimental results demonstrate that HRSC outperforms other best baseline methods by about 1% on the MovieLens and wordnet datasets in terms of node classification, and outperforms the other best baseline methods, respectively, on average by about 29.03%, 1.94%, 26.27% and 6.24% on the GPS, MovieLens, drug, and wordnet datasets in terms of link prediction.