2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-19.5
Paper Title Unveiling anomalous nodes via random sampling and consensus on graphs
Authors Vassilis N. Ioannidis, Dimitris Berberidis, Georgios B. Giannakis, University of Minnesota, United States
SessionSPTM-19: Inference over Graphs
LocationGather.Town
Session Time:Friday, 11 June, 11:30 - 12:15
Presentation Time:Friday, 11 June, 11:30 - 12:15
Presentation Poster
Topic Signal Processing Theory and Methods: [SIPG] Signal and Information Processing over Graphs
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The present paper develops a graph-based sampling and consensus (GraphSAC) approach to effectively detect anomalous nodes in large-scale graphs. GraphSAC randomly draws subsets of nodes, and relies on graph-aware criteria to judiciously filter out sets contaminated by anomalous nodes, before employing a semi-supervised learning (SSL) module to estimate nominal label distributions per node. These learned nominal distributions are minimally affected by the anomalous nodes, and hence can be directly adopted for anomaly detection. The per-draw complexity grows linearly with the number of edges, which implies efficient SSL, while draws can be run in parallel, thereby ensuring scalability to large graphs. GraphSAC is tested under different anomaly generation models based on random walks, as well as contemporary adversarial attacks for graph data. Experiments with real-world graphs showcase the advantage of GraphSAC relative to state-of-the-art alternatives.