Temporal Link Prediction Using Cluster And Temporal Information Based Motif Feature

Predicting future link states of a temporal network from past evolution is an important task from application perspective. Different from static networks analysis, temporal link prediction considers the trends of the links as time goes by, which is proved can achieve higher performance than that only exploring the interactions at a specific moment. The key challenge of this task is to obtain effective features of the evolving network. We propose a novel supervised method CTMF based on motif feature, which can characterize nearly complete local topological characteristic of node-pairs in the temporal network. We also attach cluster information as well as temporal transition factor to capture the influence of global structure and network transition on each motif over time. An unsupervised learning strategy is used on a concatenation of the cluster and temporal information based motif features (CTMF) from the continuous sets of snapshots to capture a better feature representation. We validate the effectiveness of the CTMF by utilizing it for link prediction on a number of temporal datasets, which shows our approach outperforms state-of-art methods using other features.