Distributed graph summarization

Xingjie Liu; Yuanyuan Tian; Qi He; Wang Chien Lee; John McPherson

doi:10.1145/2661829.2661862

Distributed graph summarization

Xingjie Liu, Yuanyuan Tian, Qi He, Wang Chien Lee, John McPherson

Computer Science and Engineering

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

27 Scopus citations

Abstract

Graph has been a ubiquitous and essential data representation to model real world objects and their relationships. Today, large amounts of graph data have been generated by various applications. Graph summarization techniques are crucial in uncovering useful insights about the patterns hidden in the underlying data. However, all existing works in graph summarization are single-process solutions, and as a result cannot scale to large graphs. In this paper, we introduce three distributed graph summarization algorithms to address this problem. Experimental results show that the proposed algorithms can produce good quality summaries and scale well with increasing data sizes. To the best of our knowledge, this is the first work to study distributed graph summarization methods.

Original language	English (US)
Title of host publication	CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
Publisher	Association for Computing Machinery
Pages	799-808
Number of pages	10
ISBN (Electronic)	9781450325981
DOIs	https://doi.org/10.1145/2661829.2661862
State	Published - Nov 3 2014
Event	23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China Duration: Nov 3 2014 → Nov 7 2014

Publication series

Name	CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management

Other

Other	23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
Country/Territory	China
City	Shanghai
Period	11/3/14 → 11/7/14

All Science Journal Classification (ASJC) codes

Information Systems and Management
Computer Science Applications
Information Systems

Access to Document

10.1145/2661829.2661862

Cite this

Liu, X., Tian, Y., He, Q., Lee, W. C., & McPherson, J. (2014). Distributed graph summarization. In CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management (pp. 799-808). (CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management). Association for Computing Machinery. https://doi.org/10.1145/2661829.2661862

@inproceedings{fdf14253187146558926a74c819bfde8,

title = "Distributed graph summarization",

abstract = "Graph has been a ubiquitous and essential data representation to model real world objects and their relationships. Today, large amounts of graph data have been generated by various applications. Graph summarization techniques are crucial in uncovering useful insights about the patterns hidden in the underlying data. However, all existing works in graph summarization are single-process solutions, and as a result cannot scale to large graphs. In this paper, we introduce three distributed graph summarization algorithms to address this problem. Experimental results show that the proposed algorithms can produce good quality summaries and scale well with increasing data sizes. To the best of our knowledge, this is the first work to study distributed graph summarization methods.",

author = "Xingjie Liu and Yuanyuan Tian and Qi He and Lee, {Wang Chien} and John McPherson",

year = "2014",

month = nov,

day = "3",

doi = "10.1145/2661829.2661862",

language = "English (US)",

series = "CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management",

publisher = "Association for Computing Machinery",

pages = "799--808",

booktitle = "CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management",

}

Liu, X, Tian, Y, He, Q, Lee, WC & McPherson, J 2014, Distributed graph summarization. in CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management, Association for Computing Machinery, pp. 799-808, 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, 11/3/14. https://doi.org/10.1145/2661829.2661862

Distributed graph summarization. / Liu, Xingjie; Tian, Yuanyuan; He, Qi et al.
CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, 2014. p. 799-808 (CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management).

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

TY - GEN

T1 - Distributed graph summarization

AU - Liu, Xingjie

AU - Tian, Yuanyuan

AU - He, Qi

AU - Lee, Wang Chien

AU - McPherson, John

PY - 2014/11/3

Y1 - 2014/11/3

N2 - Graph has been a ubiquitous and essential data representation to model real world objects and their relationships. Today, large amounts of graph data have been generated by various applications. Graph summarization techniques are crucial in uncovering useful insights about the patterns hidden in the underlying data. However, all existing works in graph summarization are single-process solutions, and as a result cannot scale to large graphs. In this paper, we introduce three distributed graph summarization algorithms to address this problem. Experimental results show that the proposed algorithms can produce good quality summaries and scale well with increasing data sizes. To the best of our knowledge, this is the first work to study distributed graph summarization methods.

AB - Graph has been a ubiquitous and essential data representation to model real world objects and their relationships. Today, large amounts of graph data have been generated by various applications. Graph summarization techniques are crucial in uncovering useful insights about the patterns hidden in the underlying data. However, all existing works in graph summarization are single-process solutions, and as a result cannot scale to large graphs. In this paper, we introduce three distributed graph summarization algorithms to address this problem. Experimental results show that the proposed algorithms can produce good quality summaries and scale well with increasing data sizes. To the best of our knowledge, this is the first work to study distributed graph summarization methods.

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U2 - 10.1145/2661829.2661862

DO - 10.1145/2661829.2661862

M3 - Conference contribution

AN - SCOPUS:84937442369

T3 - CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management

SP - 799

EP - 808

BT - CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management

PB - Association for Computing Machinery

T2 - 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014

Y2 - 3 November 2014 through 7 November 2014

ER -

Liu X, Tian Y, He Q, Lee WC, McPherson J. Distributed graph summarization. In CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery. 2014. p. 799-808. (CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management). doi: 10.1145/2661829.2661862

Distributed graph summarization

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