Occluded offline handwritten Chinese character inpainting via generative adversarial network and self-attention mechanism

Hypernetwork Representation Learning Based on Hyperedge Modeling

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.

Hypernetwork Representation Learning with Common Constraints of the Set and Translation

Different from conventional networks with only pairwise relationships among the nodes, there are also complex tuple relationships, namely the hyperedges among the nodes in the hypernetwork. However, most of the existing network representation learning methods cannot effectively capture the complex tuple relationships. Therefore, in order to resolve the above challenge, this paper proposes a hypernetwork representation learning method with common constraints of the set and translation, abbreviated as HRST, which incorporates both the hyperedge set associated with the nodes and the hyperedge regarded as the interaction relation among the nodes through the translation mechanism into the process of hypernetwork representation learning to obtain node representation vectors rich in the hypernetwork topology structure and hyperedge information. Experimental results on four hypernetwork datasets demonstrate that, for the node classification task, our method outperforms the other best baseline methods by about 1%. As for the link prediction task, our method is almost entirely superior to other baseline methods.