U.S. Department of EnergyOffice of Scientific and Technical Information
Multi-Level Anomaly Detection on Time-Varying Graph Data
Abstract
This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualizationmore »
- Authors:
-
- ORNL
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1214009
- DOE Contract Number:
- DE-AC05-00OR22725
- Resource Type:
- Conference
- Resource Relation:
- Conference: ASONAM 2015 - The 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Paris, France, 20150825, 20150828
- Country of Publication:
- United States
- Language:
- English
- Subject:
- anomaly detection; streaming graph; probabilitic model; random graph
Citation Formats
Bridges, Robert A, Collins, John P, Ferragut, Erik M, Laska, Jason A, and Sullivan, Blair D. Multi-Level Anomaly Detection on Time-Varying Graph Data. United States: N. p., 2015.
Web.
Bridges, Robert A, Collins, John P, Ferragut, Erik M, Laska, Jason A, & Sullivan, Blair D. Multi-Level Anomaly Detection on Time-Varying Graph Data. United States.
Bridges, Robert A, Collins, John P, Ferragut, Erik M, Laska, Jason A, and Sullivan, Blair D. 2015.
"Multi-Level Anomaly Detection on Time-Varying Graph Data". United States. https://www.osti.gov/servlets/purl/1214009.
@article{osti_1214009,
title = {Multi-Level Anomaly Detection on Time-Varying Graph Data},
author = {Bridges, Robert A and Collins, John P and Ferragut, Erik M and Laska, Jason A and Sullivan, Blair D},
abstractNote = {This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.},
doi = {},
url = {https://www.osti.gov/biblio/1214009},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}