Heterogeneous Hypernetwork Representation Learning with the Translation Constraint
LIU Zhenguo1, ZHU Yu1, ZHAO Haixing2, WANG Xiaoying1, HUANG Jianqiang1
1.Department of Computer Technology and Application, Qinghai University, Xi'ning, Qinghai 810000, China; 2.State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Xi'ning, Qinghai 810000, China
Abstract：In contrast to the ordinary network with only pairwise relationships between the nodes, there also exist complex tuple relationships (i.e. the hyperedges) among the nodes in the hypernetwork. However, most of the existing network representation learning methods cannot effectively capture complex tuple relationships. Therefore, to resolve this issue, a heterogeneous hypernetwork representation learning method with the translation constraint (HRTC) is proposed. Firstly, the proposed method combines clique expansion and star expansion to transform a heterogeneous hypernetwork abstracted as the hypergraph into a heterogeneous network abstracted as 2-section graph+incidence graph. Secondly, a meta-path walk method aware of semantic relevance of the nodes (SRwalk) is proposed to capture semantic relationships between the nodes. Finally, while the pairwise relationships between the nodes are trained, the tuple relationships among the nodes are captured by introducing the translation mechanism in knowledge representation learning. Experimental results show that as for the link prediction task, the performance of the proposed method is close to that of other optimal baseline methods, and as for the hypernetwork reconstruction task, the performance of the proposed method is better than that of other optimal baseline methods on the drug dataset for case beyond 0.6 hyperedge reconstruction ratio, meanwhile, the average performance of the proposed method outperforms that of other optimal baseline methods by 16.24% on the GPS dataset.
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2022, Vol. 36 
