Learning Subgraph Structure with LSTM for Complex Network Link Prediction

Abstract

Link prediction is a hot research topic in complex network. Traditional link prediction is based on similarities between nodes, such as common neighbors and Jaccard index. These methods are easy to understand and widely used. However, most existing works use a single relationship between two target nodes, lacking the use of information around the two target nodes. Due to the poor scalability of these methods, the performances of link prediction are not good. In this paper, we propose a novel link prediction method, learning Subgraph structure with Long-Short Term Memory network (SG-LSTM), which uses a recurrent neural network to learn the subgraph patterns to predict links. First, we extract the enclosing subgraph of the target link. Second, we use a graph labeling algorithm called the hash-based Weisfeiler-Lehman (HWL) algorithm to re-label the extracted closed subgraphs, which maps the subgraphs to the sequential data that reflects the subgraph structure. Finally, these sequential data are trained using long-short term memory network (LSTM) to learn the link prediction model. This learned LSTM model is used to predict the link. Large-scale experiments verify that our proposed method has superior link prediction performances to traditional link prediction methods.