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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 4: AAAI-22 Technical Tracks 4

Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs

February 1, 2023

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Authors

Chunpai Wang

University at Albany - SUNY


Daniel B. Neill

New York University


Feng Chen

UT Dallas


DOI:

10.1609/aaai.v36i4.20339


Abstract:

We propose a new approach, the calibrated nonparametric scan statistic (CNSS), for more accurate detection of anomalous patterns in large-scale, real-world graphs. Scan statistics identify connected subgraphs that are interesting or unexpected through maximization of a likelihood ratio statistic; in particular, nonparametric scan statistics (NPSSs) identify subgraphs with a higher than expected proportion of individually significant nodes. However, we show that recently proposed NPSS methods are miscalibrated, failing to account for the maximization of the statistic over the multiplicity of subgraphs. This results in both reduced detection power for subtle signals, and low precision of the detected subgraph even for stronger signals. Thus we develop a new statistical approach to recalibrate NPSSs, correctly adjusting for multiple hypothesis testing and taking the underlying graph structure into account. While the recalibration, based on randomization testing, is computationally expensive, we propose both an efficient (approximate) algorithm and new, closed-form lower bounds (on the expected maximum proportion of significant nodes for subgraphs of a given size, under the null hypothesis of no anomalous patterns). These advances, along with the integration of recent core-tree decomposition methods, enable CNSS to scale to large real-world graphs, with substantial improvement in the accuracy of detected subgraphs. Extensive experiments on both semi-synthetic and real-world datasets are demonstrated to validate the effectiveness of our proposed methods, in comparison with state-of-the-art counterparts.

Topics: AAAI

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HOW TO CITE:

Chunpai Wang||Daniel B. Neill||Feng Chen Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs Proceedings of the AAAI Conference on Artificial Intelligence (2022) 4201-4209.

Chunpai Wang||Daniel B. Neill||Feng Chen Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs AAAI 2022, 4201-4209.

Chunpai Wang||Daniel B. Neill||Feng Chen (2022). Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 4201-4209.

Chunpai Wang||Daniel B. Neill||Feng Chen. Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.4201-4209.

Chunpai Wang||Daniel B. Neill||Feng Chen. 2022. Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs. "Proceedings of the AAAI Conference on Artificial Intelligence". 4201-4209.

Chunpai Wang||Daniel B. Neill||Feng Chen. (2022) "Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs", Proceedings of the AAAI Conference on Artificial Intelligence, p.4201-4209

Chunpai Wang||Daniel B. Neill||Feng Chen, "Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs", AAAI, p.4201-4209, 2022.

Chunpai Wang||Daniel B. Neill||Feng Chen. "Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.4201-4209.

Chunpai Wang||Daniel B. Neill||Feng Chen. "Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 4201-4209.

Chunpai Wang||Daniel B. Neill||Feng Chen. Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs. AAAI[Internet]. 2022[cited 2023]; 4201-4209.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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