skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: 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 » 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.« less

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. 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. Thu . "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 = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2015},
month = {1}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: