Laplacian Change Point Detection for Dynamic Graphs

TL;DR

This paper introduces Laplacian Anomaly Detection (LAD), a novel method for change point detection in dynamic graphs that leverages spectral graph analysis to effectively identify anomalies over time.

Contribution

The paper presents LAD, a new spectral-based approach that models temporal dependencies in dynamic graphs for improved change point detection.

Findings

LAD outperforms existing methods in synthetic experiments.

LAD effectively detects anomalies aligned with real-world events.

Demonstrates applicability on diverse real-world dynamic networks.

Abstract

Dynamic and temporal graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for many real world applications such as intrusion identification in network systems, detection of ecosystem disturbances and detection of epidemic outbreaks. In this paper, we focus on change point detection in dynamic graphs and address two main challenges associated with this problem: I) how to compare graph snapshots across time, II) how to capture temporal dependencies. To solve the above challenges, we propose Laplacian Anomaly Detection (LAD) which uses the spectrum of the Laplacian matrix of the graph structure at each snapshot to obtain low dimensional embeddings. LAD explicitly models short term and long term dependencies by applying two sliding windows. In synthetic experiments, LAD outperforms the state-of-the-art method. We also evaluate our method on three real dynamic networks: UCI message network, US senate co-sponsorship network and Canadian bill voting network. In all three datasets, we demonstrate that our method can more effectively identify anomalous time points according to significant real world events.