Link Prediction Model for Dynamic Graphs

Abstract

In the real world, any complex relationships can be represented as graphs, such as communication networks, biological networks, recommendation systems, etc. Link prediction is an important research topic in the field of graphs, but most of the current link prediction models are only for static graphs, and they ignore the evolution pattern of graphs in the time domain and the importance of global features in the evolution process. To this end, this paper proposes a link prediction model for dynamic graphs. First, in order to obtain high-quality global features, the model uses adversarial training to optimize the mutual information loss of global features and higher-order local features. Then a perceptual model based on a wide smooth stochastic process is used to ensure the smoothness of the global features in the time domain by constraining the mean and autocorrelation function values of the global features in the time dimension. The evolution pattern of the dynamic graph is then captured using a long and short-term memory (LSTM) network. Finally, the loss of predicted and true values is optimized using adversarial networks. The experimental results on USCB, SBM and AS datasets show that the proposed model performs well in the link prediction task of dynamic graphs, and it not only significantly improves the AUC values, but also reduces the MSE values. Also, the results of the ablation experiments show that the local features contribute to the global feature ground extraction, and the quality and smoothness of the global features play an important role in network link prediction.

Keywords

• |dynamic graphs|link prediction|mutual information|adversarial networks|long short-term memory networks (lstm)