  Graph-based change point detection (CPD) play an irreplaceable role in
discovering anomalous graphs in the 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 ones, 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 having the
evolving patterns, 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 the effectiveness and superiority of our model.

Social and Information Networks;Artificial Intelligence