Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing

Corentin Larroche; Johan Mazel; Stephan Clémençon

doi:10.14428/esann/2021.ES2021-32

Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing

Corentin Larroche
, Johan Mazel
, Stephan Clémençon

ANSSI
Institut Polytechnique de Paris

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

We propose a computationally efficient procedure for elevated mean detection on a connected subgraph of a network with node-related scalar observations. Our approach relies on two intuitions: first, a significant concentration of high observations in a connected subgraph implies that the subgraph induced by the nodes associated with the highest observations has a large connected component. Secondly, a greater detection power can be obtained in certain cases by denoising the observations using the network structure. Numerical experiments show that our procedure's detection performance and computational efficiency are both competitive.

langue originale	Anglais
titre	ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Editeur	i6doc.com publication
Pages	399-404
Nombre de pages	6
ISBN (Electronique)	9782875870827
Les DOIs	https://doi.org/10.14428/esann/2021.ES2021-32
état	Publié - 1 janv. 2021
Evénement	29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021 - Virtual, Online, Belgique Durée: 6 oct. 2021 → 8 oct. 2021

Série de publications

Nom	ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Une conférence

Une conférence	29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021
Pays/Territoire	Belgique
La ville	Virtual, Online
période	6/10/21 → 8/10/21

Accès au document

10.14428/esann/2021.ES2021-32

Contient cette citation

Larroche, C., Mazel, J., & Clémençon, S. (2021). Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing. Dans ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (p. 399-404). (ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). i6doc.com publication. https://doi.org/10.14428/esann/2021.ES2021-32

Larroche, Corentin ; Mazel, Johan ; Clémençon, Stephan. / Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing. ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2021. p. 399-404 (ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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title = "Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing",

abstract = "We propose a computationally efficient procedure for elevated mean detection on a connected subgraph of a network with node-related scalar observations. Our approach relies on two intuitions: first, a significant concentration of high observations in a connected subgraph implies that the subgraph induced by the nodes associated with the highest observations has a large connected component. Secondly, a greater detection power can be obtained in certain cases by denoising the observations using the network structure. Numerical experiments show that our procedure's detection performance and computational efficiency are both competitive.",

author = "Corentin Larroche and Johan Mazel and Stephan Cl{\'e}men{\c c}on",

note = "Publisher Copyright: {\textcopyright} 2021 ESANN Intelligence and Machine Learning. All rights reserved.; 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021 ; Conference date: 06-10-2021 Through 08-10-2021",

year = "2021",

month = jan,

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doi = "10.14428/esann/2021.ES2021-32",

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}

Larroche, C, Mazel, J & Clémençon, S 2021, Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing. Dans ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, i6doc.com publication, p. 399-404, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021, Virtual, Online, Belgique, 6/10/21. https://doi.org/10.14428/esann/2021.ES2021-32

Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing. / Larroche, Corentin; Mazel, Johan; Clémençon, Stephan.
ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2021. p. 399-404 (ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing

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AU - Mazel, Johan

AU - Clémençon, Stephan

PY - 2021/1/1

Y1 - 2021/1/1

N2 - We propose a computationally efficient procedure for elevated mean detection on a connected subgraph of a network with node-related scalar observations. Our approach relies on two intuitions: first, a significant concentration of high observations in a connected subgraph implies that the subgraph induced by the nodes associated with the highest observations has a large connected component. Secondly, a greater detection power can be obtained in certain cases by denoising the observations using the network structure. Numerical experiments show that our procedure's detection performance and computational efficiency are both competitive.

AB - We propose a computationally efficient procedure for elevated mean detection on a connected subgraph of a network with node-related scalar observations. Our approach relies on two intuitions: first, a significant concentration of high observations in a connected subgraph implies that the subgraph induced by the nodes associated with the highest observations has a large connected component. Secondly, a greater detection power can be obtained in certain cases by denoising the observations using the network structure. Numerical experiments show that our procedure's detection performance and computational efficiency are both competitive.

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DO - 10.14428/esann/2021.ES2021-32

M3 - Conference contribution

AN - SCOPUS:85129239833

T3 - ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

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BT - ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

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Y2 - 6 October 2021 through 8 October 2021

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Larroche C, Mazel J, Clémençon S. Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing. Dans ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication. 2021. p. 399-404. (ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). doi: 10.14428/esann/2021.ES2021-32

Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing

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