Latent evolution model for change point detection in time-varying networks

Abstract

Graph-based change point detection (CPD) plays an irreplaceable role in discovering anomalous graphs in a time-varying network. While several techniques have been proposed to detect change points by identifying whether there is a significant difference between the target network and successive previous networks, they neglect the natural evolution of the network. In practice, real-world graphs such as social networks, traffic networks, and rating networks are constantly evolving over time. Considering this problem, we treat the problem as a prediction task and propose a novel CPD method for dynamic graphs via a latent evolution model. Our method focuses on learning the low-dimensional representations of networks and capturing the evolving patterns of these learned latent representations simultaneously. After the evolving patterns are learned, a prediction of the target network can be achieved. Then, we can detect the change points by comparing the prediction and the actual network by leveraging a trade-off strategy, which balances the importance between the prediction network and the normal graph pattern extracted from previous networks. Intensive experiments conducted on both synthetic and real-world datasets show that our model is up to 12% better than the baseline methods in terms of the average detection accuracy.