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The case for anomalous link discovery

Authors:

Matthew J. Rattigan,

David JensenAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 7, Issue 2

Pages 41 - 47

https://doi.org/10.1145/1117454.1117460

Published: 01 December 2005 Publication History

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Abstract

In this paper, we describe the challenges inherent to the task of link prediction, and we analyze one reason why many link prediction models perform poorly. Specifically, we demonstrate the effects of the extremely large class skew associated with the link prediction task. We then present an alternate task --- anomalous link discovery (ALD) --- and qualitatively demonstrate the effectiveness of simple link prediction models for the ALD task. We show that even the simplistic structural models that perform poorly on link prediction can perform quite well at the ALD task.

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Xu XWang HLal AGunter CLi B(2023)EDoG: Adversarial Edge Detection For Graph Neural Networks2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)10.1109/SaTML54575.2023.00027(291-305)Online publication date: Feb-2023
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Atzmueller MKanawati R(2022)Explainability in Cyber Security using Complex Network Analysis: A Brief Methodological OverviewProceedings of the 2022 European Interdisciplinary Cybersecurity Conference10.1145/3528580.3532839(49-52)Online publication date: 15-Jun-2022
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Feroze ADaud AAmjad THayat M(2021)Group Anomaly Detection: Past Notions, Present Insights, and Future ProspectsSN Computer Science10.1007/s42979-021-00603-x2:3Online publication date: 16-Apr-2021
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The case for anomalous link discovery

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Published In

ACM SIGKDD Explorations Newsletter Volume 7, Issue 2

December 2005

152 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/1117454

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Association for Computing Machinery

New York, NY, United States

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Published: 01 December 2005

Published in SIGKDD Volume 7, Issue 2

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Xu XWang HLal AGunter CLi B(2023)EDoG: Adversarial Edge Detection For Graph Neural Networks2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)10.1109/SaTML54575.2023.00027(291-305)Online publication date: Feb-2023
https://doi.org/10.1109/SaTML54575.2023.00027
Atzmueller MKanawati R(2022)Explainability in Cyber Security using Complex Network Analysis: A Brief Methodological OverviewProceedings of the 2022 European Interdisciplinary Cybersecurity Conference10.1145/3528580.3532839(49-52)Online publication date: 15-Jun-2022
https://dl.acm.org/doi/10.1145/3528580.3532839
Feroze ADaud AAmjad THayat M(2021)Group Anomaly Detection: Past Notions, Present Insights, and Future ProspectsSN Computer Science10.1007/s42979-021-00603-x2:3Online publication date: 16-Apr-2021
https://dl.acm.org/doi/10.1007/s42979-021-00603-x
Coşkun MBaggag AKoyutürk M(2021)Fast computation of Katz index for efficient processing of link prediction queriesData Mining and Knowledge Discovery10.1007/s10618-021-00754-835:4(1342-1368)Online publication date: 1-Jul-2021
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Daud NAb Hamid SSaadoon MSahran FAnuar N(2020)Applications of link prediction in social networks: A reviewJournal of Network and Computer Applications10.1016/j.jnca.2020.102716166(102716)Online publication date: Sep-2020
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Atzmueller M(2019)Onto Model-based Anomalous Link Pattern Mining on Feature-Rich Social Interaction NetworksCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316707(1047-1050)Online publication date: 13-May-2019
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Pairwise link prediction

Noise-Enhanced Unsupervised Link Prediction

A new topological metric for link prediction in directed, weighted and temporal networks

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