Latent Evolution Model for Change Point Detection in Time-varying Networks

• URL: http://arxiv.org/abs/2212.08818v1
• Date: Sat, 17 Dec 2022 07:39:35 GMT
• Title: Latent Evolution Model for Change Point Detection in Time-varying Networks
• Authors: Yongshun Gong, Xue Dong, Jian Zhang, Meng Chen
• Abstract summary: Graph-based change point detection (CPD) play an irreplaceable role in discovering anomalous graphs in the time-varying network. In practice, real-world graphs such as social networks, traffic networks, and rating networks are constantly evolving over time. We propose a novel CPD method for dynamic graphs via a latent evolution model.
• Score: 11.442584422147368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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