Link prediction model based on directed hypergraph adaptive convolution
Although diverse solutions for link prediction have been provided by Graph Neural Networks(GNN),the recent models have significant shortcomings in fully utilizing high-order and asymmetric information between vertices caused by the structural constraints of ordinary graphs.To address the above issues,a link prediction model based on directed hypergraph adaptive convolution was proposed.Firstly,the directed hypergraph structure was employed to represent high-order and directional information between vertices more sufficiently,possessing advantages of both hypergraphs and directed graphs.Secondly,an adaptive information propagation method was adopted by directed hypergraph adaptive convolution to replace the directional information propagation method in traditional directed hypergraphs,thereby solving the problem of ineffective updating of embeddings for tail vertices of directed hyperedges,and solving the problem of excessive smoothing of vertices caused by multi-layer convolution.Experimental results based on explicit vertex features on Citeseer dataset show that the proposed model achieves a 2.23 percentage points increase in the Area Under the ROC(Receiver Operating Characteristic)Curve(AUC)and a 1.31 percentage points increase in Average Precision(AP)compared to the Directed Hypergraph Neural Network(DHNN)model in link prediction task.Therefore,it can be concluded that this model expresses the relationships between vertices adequately and improves the accuracy of link prediction task effectively.
万方数据
维普
