Anomalous Edge Detection in Edge Exchangeable Social Network Models

Rui Luo; Buddhika Nettasinghe; Vikram Krishnamurthy

doi:10.48550/arxiv.2109.12727

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Anomalous Edge Detection in Edge Exchangeable Social Network Models

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Anomalous Edge Detection in Edge Exchangeable Social Network Models

Rui Luo, Buddhika Nettasinghe and Vikram Krishnamurthy

ArXiv.org

09/26/2021

DOI: 10.48550/arxiv.2109.12727

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https://doi.org/10.48550/arxiv.2109.12727View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

This paper studies detecting anomalous edges in directed graphs that model social networks. We exploit edge exchangeability as a criterion for distinguishing anomalous edges from normal edges. Then we present an anomaly detector based on conformal prediction theory; this detector has a guaranteed upper bound for false positive rate. In numerical experiments, we show that the proposed algorithm achieves superior performance to baseline methods.

Details

Title: Subtitle: Anomalous Edge Detection in Edge Exchangeable Social Network Models
Creators: Rui Luo
Buddhika Nettasinghe
Vikram Krishnamurthy
Resource Type: Preprint
Publication Details: ArXiv.org
DOI: 10.48550/arxiv.2109.12727
ISSN: 2331-8422
Language: English
Date posted: 09/26/2021
Academic Unit: Business Analytics
Record Identifier: 9984423766002771

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