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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Aleksandar Bojchevski

Technical University of Munich


Stephan Günnemann

Technical University of Munich


DOI:

10.1609/aaai.v32i1.11642


Abstract:

We study the problem of robust attributed graph clustering. In real data, the clustering structure is often obfuscated due to anomalies or corruptions. While robust methods have been recently introduced that handle anomalies as part of the clustering process, they all fail to account for one core aspect: Since attributed graphs consist of two views (network structure and attributes) anomalies might materialize only partially, i.e. instances might be corrupted in one view but perfectly fit in the other. In this case, we can still derive meaningful cluster assignments. Existing works only consider complete anomalies. In this paper, we present a novel probabilistic generative model (PAICAN) that explicitly models partial anomalies by generalizing ideas of Degree Corrected Stochastic Block Models and Bernoulli Mixture Models. We provide a highly scalable variational inference approach with runtime complexity linear in the number of edges. The robustness of our model w.r.t. anomalies is demonstrated by our experimental study, outperforming state-of-the-art competitors.

Topics: AAAI

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HOW TO CITE:

Aleksandar Bojchevski||Stephan Günnemann Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Aleksandar Bojchevski||Stephan Günnemann Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure AAAI 2018, .

Aleksandar Bojchevski||Stephan Günnemann (2018). Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Aleksandar Bojchevski||Stephan Günnemann. Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Aleksandar Bojchevski||Stephan Günnemann. 2018. Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Aleksandar Bojchevski||Stephan Günnemann. (2018) "Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Aleksandar Bojchevski||Stephan Günnemann, "Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure", AAAI, p., 2018.

Aleksandar Bojchevski||Stephan Günnemann. "Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Aleksandar Bojchevski||Stephan Günnemann. "Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Aleksandar Bojchevski||Stephan Günnemann. Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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