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

TL;DR

This paper introduces a novel change point detection method for dynamic networks that models the natural evolution of graphs through latent representations, improving detection accuracy in real-world scenarios.

Contribution

It proposes a latent evolution model that captures evolving patterns in low-dimensional network representations for more effective change point detection.

Findings

Outperforms existing methods on synthetic datasets

Effective in real-world social and traffic networks

Accurately detects change points with high precision

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.