Local graph clustering by multi-network random walk with restart

Yaowei Yan, Dongsheng Luo, Jingchao Ni, Hongliang Fei, Wei Fan, Xiong Yu, John Yen, Xiang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Searching local graph clusters is an important problem in big network analysis. Given a query node in a graph, local clustering aims at finding a subgraph around the query node, which consists of nodes highly relevant to the query node. Existing local clustering methods are based on single networks that contain limited information. In contrast, the real data are always comprehensive and can be represented better by multiple connected networks (multi-network). To take the advantage of heterogeneity of multi-network and improve the clustering accuracy, we advance a strategy for local graph clustering based on Multi-network Random Walk with Restart (MRWR), which discovers local clusters on a target network in association with additional networks. For the proposed local clustering method, we develop a localized approximate algorithm (AMRWR) on solid theoretical basis to speed up the searching process. To the best of our knowledge, this is the first elaboration of local clustering on a target network by integrating multiple networks. Empirical evaluations show that the proposed method improves clustering accuracy by more than 10% on average with competently short running time, compared with the alternative state-of-the-art graph clustering approaches.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings
EditorsGeoffrey I. Webb, Dinh Phung, Mohadeseh Ganji, Lida Rashidi, Vincent S. Tseng, Bao Ho
PublisherSpringer Verlag
Pages490-501
Number of pages12
ISBN (Print)9783319930398
DOIs
StatePublished - Jan 1 2018
Event22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia
Duration: Jun 3 2018Jun 6 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10939 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018
CountryAustralia
CityMelbourne
Period6/3/186/6/18

Fingerprint

Graph Clustering
Restart
Electric network analysis
Random walk
Clustering Methods
Clustering
Query
Vertex of a graph
Target
Approximate Algorithm
Network Analysis
Graph in graph theory
Subgraph
Speedup
Alternatives
Evaluation

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Yan, Y., Luo, D., Ni, J., Fei, H., Fan, W., Yu, X., ... Zhang, X. (2018). Local graph clustering by multi-network random walk with restart. In G. I. Webb, D. Phung, M. Ganji, L. Rashidi, V. S. Tseng, & B. Ho (Eds.), Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings (pp. 490-501). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10939 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-93040-4_39
Yan, Yaowei ; Luo, Dongsheng ; Ni, Jingchao ; Fei, Hongliang ; Fan, Wei ; Yu, Xiong ; Yen, John ; Zhang, Xiang. / Local graph clustering by multi-network random walk with restart. Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. editor / Geoffrey I. Webb ; Dinh Phung ; Mohadeseh Ganji ; Lida Rashidi ; Vincent S. Tseng ; Bao Ho. Springer Verlag, 2018. pp. 490-501 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "Searching local graph clusters is an important problem in big network analysis. Given a query node in a graph, local clustering aims at finding a subgraph around the query node, which consists of nodes highly relevant to the query node. Existing local clustering methods are based on single networks that contain limited information. In contrast, the real data are always comprehensive and can be represented better by multiple connected networks (multi-network). To take the advantage of heterogeneity of multi-network and improve the clustering accuracy, we advance a strategy for local graph clustering based on Multi-network Random Walk with Restart (MRWR), which discovers local clusters on a target network in association with additional networks. For the proposed local clustering method, we develop a localized approximate algorithm (AMRWR) on solid theoretical basis to speed up the searching process. To the best of our knowledge, this is the first elaboration of local clustering on a target network by integrating multiple networks. Empirical evaluations show that the proposed method improves clustering accuracy by more than 10{\%} on average with competently short running time, compared with the alternative state-of-the-art graph clustering approaches.",
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Yan, Y, Luo, D, Ni, J, Fei, H, Fan, W, Yu, X, Yen, J & Zhang, X 2018, Local graph clustering by multi-network random walk with restart. in GI Webb, D Phung, M Ganji, L Rashidi, VS Tseng & B Ho (eds), Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10939 LNAI, Springer Verlag, pp. 490-501, 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018, Melbourne, Australia, 6/3/18. https://doi.org/10.1007/978-3-319-93040-4_39

Local graph clustering by multi-network random walk with restart. / Yan, Yaowei; Luo, Dongsheng; Ni, Jingchao; Fei, Hongliang; Fan, Wei; Yu, Xiong; Yen, John; Zhang, Xiang.

Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. ed. / Geoffrey I. Webb; Dinh Phung; Mohadeseh Ganji; Lida Rashidi; Vincent S. Tseng; Bao Ho. Springer Verlag, 2018. p. 490-501 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10939 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Local graph clustering by multi-network random walk with restart

AU - Yan, Yaowei

AU - Luo, Dongsheng

AU - Ni, Jingchao

AU - Fei, Hongliang

AU - Fan, Wei

AU - Yu, Xiong

AU - Yen, John

AU - Zhang, Xiang

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N2 - Searching local graph clusters is an important problem in big network analysis. Given a query node in a graph, local clustering aims at finding a subgraph around the query node, which consists of nodes highly relevant to the query node. Existing local clustering methods are based on single networks that contain limited information. In contrast, the real data are always comprehensive and can be represented better by multiple connected networks (multi-network). To take the advantage of heterogeneity of multi-network and improve the clustering accuracy, we advance a strategy for local graph clustering based on Multi-network Random Walk with Restart (MRWR), which discovers local clusters on a target network in association with additional networks. For the proposed local clustering method, we develop a localized approximate algorithm (AMRWR) on solid theoretical basis to speed up the searching process. To the best of our knowledge, this is the first elaboration of local clustering on a target network by integrating multiple networks. Empirical evaluations show that the proposed method improves clustering accuracy by more than 10% on average with competently short running time, compared with the alternative state-of-the-art graph clustering approaches.

AB - Searching local graph clusters is an important problem in big network analysis. Given a query node in a graph, local clustering aims at finding a subgraph around the query node, which consists of nodes highly relevant to the query node. Existing local clustering methods are based on single networks that contain limited information. In contrast, the real data are always comprehensive and can be represented better by multiple connected networks (multi-network). To take the advantage of heterogeneity of multi-network and improve the clustering accuracy, we advance a strategy for local graph clustering based on Multi-network Random Walk with Restart (MRWR), which discovers local clusters on a target network in association with additional networks. For the proposed local clustering method, we develop a localized approximate algorithm (AMRWR) on solid theoretical basis to speed up the searching process. To the best of our knowledge, this is the first elaboration of local clustering on a target network by integrating multiple networks. Empirical evaluations show that the proposed method improves clustering accuracy by more than 10% on average with competently short running time, compared with the alternative state-of-the-art graph clustering approaches.

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Yan Y, Luo D, Ni J, Fei H, Fan W, Yu X et al. Local graph clustering by multi-network random walk with restart. In Webb GI, Phung D, Ganji M, Rashidi L, Tseng VS, Ho B, editors, Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. Springer Verlag. 2018. p. 490-501. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-93040-4_39