Local Partition in Rich Graphs

Scott Freitas; Nan Cao; Yinglong Xia; Duen Horng Polo Chau; Hanghang Tong

doi:10.1109/BigData.2018.8622227

Local Partition in Rich Graphs

Scott Freitas, Nan Cao, Yinglong Xia, Duen Horng Polo Chau, Hanghang Tong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

Local graph partitioning is a key graph mining tool that allows researchers to identify small groups of interrelated nodes (e.g., people) and their connective edges (e.g., interactions). As local graph partitioning focuses primarily on the graph structure (vertices and edges), it often fails to consider the additional information contained in the attributes. We propose a scalable algorithm to improve local graph partitioning by taking into account both the graph structure and attributes. Experimental results show that our proposed AttriPart algorithm finds up to 1.6× denser local partitions, while running approximately 43× faster than traditional local partitioning techniques (PageRank-Nibble).

Original language	English (US)
Title of host publication	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
Editors	Yang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1001-1008
Number of pages	8
ISBN (Electronic)	9781538650356
DOIs	https://doi.org/10.1109/BigData.2018.8622227
State	Published - Jan 22 2019
Event	2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States Duration: Dec 10 2018 → Dec 13 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference	2018 IEEE International Conference on Big Data, Big Data 2018
Country/Territory	United States
City	Seattle
Period	12/10/18 → 12/13/18

Keywords

attributes
conductance
local partition
pagerank
rich graph
subgraph

ASJC Scopus subject areas

Computer Science Applications
Information Systems

Access to Document

10.1109/BigData.2018.8622227

Cite this

Freitas, S., Cao, N., Xia, Y., Chau, D. H. P., & Tong, H. (2019). Local Partition in Rich Graphs. In Y. Song, B. Liu, K. Lee, N. Abe, C. Pu, M. Qiao, N. Ahmed, D. Kossmann, J. Saltz, J. Tang, J. He, H. Liu, & X. Hu (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 1001-1008). Article 8622227 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2018.8622227

Local Partition in Rich Graphs. / Freitas, Scott; Cao, Nan; Xia, Yinglong et al.
Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. ed. / Yang Song; Bing Liu; Kisung Lee; Naoki Abe; Calton Pu; Mu Qiao; Nesreen Ahmed; Donald Kossmann; Jeffrey Saltz; Jiliang Tang; Jingrui He; Huan Liu; Xiaohua Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1001-1008 8622227 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Freitas, S, Cao, N, Xia, Y, Chau, DHP & Tong, H 2019, Local Partition in Rich Graphs. in Y Song, B Liu, K Lee, N Abe, C Pu, M Qiao, N Ahmed, D Kossmann, J Saltz, J Tang, J He, H Liu & X Hu (eds), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018., 8622227, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1001-1008, 2018 IEEE International Conference on Big Data, Big Data 2018, Seattle, United States, 12/10/18. https://doi.org/10.1109/BigData.2018.8622227

Freitas S, Cao N, Xia Y, Chau DHP, Tong H. Local Partition in Rich Graphs. In Song Y, Liu B, Lee K, Abe N, Pu C, Qiao M, Ahmed N, Kossmann D, Saltz J, Tang J, He J, Liu H, Hu X, editors, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1001-1008. 8622227. (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). doi: 10.1109/BigData.2018.8622227

Freitas, Scott ; Cao, Nan ; Xia, Yinglong et al. / Local Partition in Rich Graphs. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. editor / Yang Song ; Bing Liu ; Kisung Lee ; Naoki Abe ; Calton Pu ; Mu Qiao ; Nesreen Ahmed ; Donald Kossmann ; Jeffrey Saltz ; Jiliang Tang ; Jingrui He ; Huan Liu ; Xiaohua Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1001-1008 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

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author = "Scott Freitas and Nan Cao and Yinglong Xia and Chau, {Duen Horng Polo} and Hanghang Tong",

note = "Funding Information: VII. ACKNOWLEDGEMENTS This work is supported by NSF (IIS-1651203, IIS-1715385, IIS-1743040, IIS-1563816 and DGE-1650044), DTRA (HDTRA1-16-0017), ARO (W911NF-16-1-0168), DHS (2017-ST-061-QA0001), NSFC Grants (61602306), Fundamental Research Funds for the Central Universities, and gifts from Huawei and Baidu. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Big Data, Big Data 2018 ; Conference date: 10-12-2018 Through 13-12-2018",

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N1 - Funding Information: VII. ACKNOWLEDGEMENTS This work is supported by NSF (IIS-1651203, IIS-1715385, IIS-1743040, IIS-1563816 and DGE-1650044), DTRA (HDTRA1-16-0017), ARO (W911NF-16-1-0168), DHS (2017-ST-061-QA0001), NSFC Grants (61602306), Fundamental Research Funds for the Central Universities, and gifts from Huawei and Baidu. Publisher Copyright: © 2018 IEEE.

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N2 - Local graph partitioning is a key graph mining tool that allows researchers to identify small groups of interrelated nodes (e.g., people) and their connective edges (e.g., interactions). As local graph partitioning focuses primarily on the graph structure (vertices and edges), it often fails to consider the additional information contained in the attributes. We propose a scalable algorithm to improve local graph partitioning by taking into account both the graph structure and attributes. Experimental results show that our proposed AttriPart algorithm finds up to 1.6× denser local partitions, while running approximately 43× faster than traditional local partitioning techniques (PageRank-Nibble).

AB - Local graph partitioning is a key graph mining tool that allows researchers to identify small groups of interrelated nodes (e.g., people) and their connective edges (e.g., interactions). As local graph partitioning focuses primarily on the graph structure (vertices and edges), it often fails to consider the additional information contained in the attributes. We propose a scalable algorithm to improve local graph partitioning by taking into account both the graph structure and attributes. Experimental results show that our proposed AttriPart algorithm finds up to 1.6× denser local partitions, while running approximately 43× faster than traditional local partitioning techniques (PageRank-Nibble).

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Local Partition in Rich Graphs

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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