2020/07/02 by Shenyang Huang, Yasmeen Hitti, Guillaume Rabusseau +1 · 55 citations
Computer Science · Mathematics · Medicine · Physics and Astronomy · #Anomaly Detection Techniques and Applications #Anomaly detection #Change detection #Complex Network Analysis Techniques #Data-Driven Disease Surveillance #Graph #Intrusion detection system #Laplace operator #Laplacian matrix #Snapshot (computer storage) #Voting #cs.LG #cs.SI #stat.ML
paper · pdf · doi:10.1145/3394486.3403077
in KDD 2020, 10 pages
arxiv created 2020/07/02 · arxiv updated 2020/07/03 · openalex created_date 2020/07/10 · openalex publication_date 2020/08/20 · openalex updated_date 2026/08/05
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.