Discovering temporal communities from social network documents

Ding Zhou; Isaac Councill; Hongyuan Zha; C. Lee Giles

doi:10.1109/ICDM.2007.56

Discovering temporal communities from social network documents

Ding Zhou, Isaac Councill, Hongyuan Zha, C. Lee Giles

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

67 Scopus citations

Abstract

This paper studies the discovery of communities from social network documents produced over time, addressing the discovery of temporal trends in community memberships. We first formulate static community discovery at a single time period as a tripartite graph partitioning problem. Then we propose to discover the temporal communities by threading the statically derived communities in different time periods using a new constrained partitioning algorithm, which partitions graphs based on topology as well as prior information regarding vertex membership. We evaluate the proposed approach on synthetic datasets and a real-world dataset prepared from the CiteSeer.

Original language	English (US)
Title of host publication	Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	745-750
Number of pages	6
ISBN (Print)	0769530184, 9780769530185
DOIs	https://doi.org/10.1109/ICDM.2007.56
State	Published - 2007
Event	7th IEEE International Conference on Data Mining, ICDM 2007 - Omaha, NE, United States Duration: Oct 28 2007 → Oct 31 2007

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Other

Other	7th IEEE International Conference on Data Mining, ICDM 2007
Country/Territory	United States
City	Omaha, NE
Period	10/28/07 → 10/31/07

All Science Journal Classification (ASJC) codes

General Engineering

Access to Document

10.1109/ICDM.2007.56

Cite this

Zhou, D., Councill, I., Zha, H., & Giles, C. L. (2007). Discovering temporal communities from social network documents. In Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007 (pp. 745-750). Article 4470321 (Proceedings - IEEE International Conference on Data Mining, ICDM). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2007.56

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title = "Discovering temporal communities from social network documents",

abstract = "This paper studies the discovery of communities from social network documents produced over time, addressing the discovery of temporal trends in community memberships. We first formulate static community discovery at a single time period as a tripartite graph partitioning problem. Then we propose to discover the temporal communities by threading the statically derived communities in different time periods using a new constrained partitioning algorithm, which partitions graphs based on topology as well as prior information regarding vertex membership. We evaluate the proposed approach on synthetic datasets and a real-world dataset prepared from the CiteSeer.",

author = "Ding Zhou and Isaac Councill and Hongyuan Zha and Giles, {C. Lee}",

year = "2007",

doi = "10.1109/ICDM.2007.56",

language = "English (US)",

isbn = "0769530184",

series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

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address = "United States",

}

Zhou, D, Councill, I, Zha, H & Giles, CL 2007, Discovering temporal communities from social network documents. in Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007., 4470321, Proceedings - IEEE International Conference on Data Mining, ICDM, Institute of Electrical and Electronics Engineers Inc., pp. 745-750, 7th IEEE International Conference on Data Mining, ICDM 2007, Omaha, NE, United States, 10/28/07. https://doi.org/10.1109/ICDM.2007.56

Discovering temporal communities from social network documents. / Zhou, Ding; Councill, Isaac; Zha, Hongyuan et al.
Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007. Institute of Electrical and Electronics Engineers Inc., 2007. p. 745-750 4470321 (Proceedings - IEEE International Conference on Data Mining, ICDM).

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

TY - GEN

T1 - Discovering temporal communities from social network documents

AU - Zhou, Ding

AU - Councill, Isaac

AU - Zha, Hongyuan

AU - Giles, C. Lee

PY - 2007

Y1 - 2007

N2 - This paper studies the discovery of communities from social network documents produced over time, addressing the discovery of temporal trends in community memberships. We first formulate static community discovery at a single time period as a tripartite graph partitioning problem. Then we propose to discover the temporal communities by threading the statically derived communities in different time periods using a new constrained partitioning algorithm, which partitions graphs based on topology as well as prior information regarding vertex membership. We evaluate the proposed approach on synthetic datasets and a real-world dataset prepared from the CiteSeer.

AB - This paper studies the discovery of communities from social network documents produced over time, addressing the discovery of temporal trends in community memberships. We first formulate static community discovery at a single time period as a tripartite graph partitioning problem. Then we propose to discover the temporal communities by threading the statically derived communities in different time periods using a new constrained partitioning algorithm, which partitions graphs based on topology as well as prior information regarding vertex membership. We evaluate the proposed approach on synthetic datasets and a real-world dataset prepared from the CiteSeer.

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M3 - Conference contribution

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SN - 9780769530185

T3 - Proceedings - IEEE International Conference on Data Mining, ICDM

SP - 745

EP - 750

BT - Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th IEEE International Conference on Data Mining, ICDM 2007

Y2 - 28 October 2007 through 31 October 2007

ER -

Discovering temporal communities from social network documents

Abstract

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All Science Journal Classification (ASJC) codes

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