Dynamic Network Representation Learning Method Based on Improved GRU Network

Abstract

As social networks have been rapidly growing, traditional network representation learning methods are struggling to accurately characterize their dynamic changes, and to output effective node classification and link prediction. To address this problem, this paper proposes IproGRU, a dynamic network representation learning method based on an improved Gated Recurrent Unit (GRU) network to improve the dynamic network representation. First, the method quickly generates embedding for an influenced node by sampling and aggregating features of its neighboring nodes when the network changes. Second, it updates the embedding of the influenced node on time series by the improved GRU network to fully adapt to the changes of the dynamic network. Experimental results on node classification and link prediction for three datasets of dynamic networks show that the proposed method improves the accuracy by 5–10 % on average from those of the traditional Node2vec and GraphSAGE methods and has a slight advantage over Graph Convolutional Networks (GCNs). The results demonstrate that our method is effective for dynamic network representation.