Dynamic Network Link Prediction Based on Sequential Graph Convolution

Dynamic network link prediction has become a hot topic in network science field because of its wide application prospect. However, the complexity of spatial correlation and temporal dependence in the evolution process of dynamic network links leads to the great challenges of dynamic network link prediction task. In this paper, a dynamic network link prediction model based on sequential graph convolution (DNLP-SGC) is proposed. On the one hand, because network snapshot sequence cannot effectively reflect the continuity of dynamic network evolution, the edge trigger mechanism is employed to modify the original network weight matrix, so as to make up loss timing information in discrete snapshot of dynamic network. On the other hand, from the view of network evolution and considering the feature similarity and historical interaction information between nodes, a temporal graph convolution method is proposed to extract node features in dynamic network, and the method integrates the spatial-temporal dependence of nodes effectively. Furthermore, the causal convolutional network is used to capture the potential global temporal features in the dynamic network evolution process to achieve dynamic network link prediction. Experimental results on two real dynamic network datasets show that DNLP-SGC outperforms the baseline model on three common indexes, such as precision, recall and AUC.