Community detection with prior knowledge

Karthik Subbian, Charu C. Aggarwal, Jaideep Srivastava, Philip S. Yu

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

7 Citations (Scopus)

Abstract

The problem of community detection is a challenging one because of the presence of hubs and noisy links, which tend to create highly imbalanced graph clusters. Often, these resulting clusters are not very intuitive and difficult to interpret. With the growing availability of network information, there is a significant amount of prior knowledge available about the communities in social, communication and several other networks. These community labels may be noisy and very limited, though they do help in community detection. In this paper, we explore the use of such noisy labeled information for finding high quality communities. We will present an adaptive density-based clustering which allows flexible incorporation of prior knowledge in to the community detection process. We use a random walk framework to compute the node densities and the level of supervision regulates the node densities and the quality of resulting density based clusters. Our framework is general enough to produce both overlapping and non-overlapping clusters. We empirically show that even with a tiny amount of supervision, our approach can produce superior communities compared to popular baselines.

Original languageEnglish
Title of host publicationProceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013
PublisherSiam Society
Pages405-413
Number of pages9
ISBN (Print)9781611972627
Publication statusPublished - 2013
Externally publishedYes
EventSIAM International Conference on Data Mining, SDM 2013 - Austin, United States
Duration: 2 May 20134 May 2013

Other

OtherSIAM International Conference on Data Mining, SDM 2013
CountryUnited States
CityAustin
Period2/5/134/5/13

Fingerprint

Labels
Availability
Communication

Keywords

  • Clusters
  • Communities
  • Supervision

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Cite this

Subbian, K., Aggarwal, C. C., Srivastava, J., & Yu, P. S. (2013). Community detection with prior knowledge. In Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013 (pp. 405-413). Siam Society.

Community detection with prior knowledge. / Subbian, Karthik; Aggarwal, Charu C.; Srivastava, Jaideep; Yu, Philip S.

Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. Siam Society, 2013. p. 405-413.

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

Subbian, K, Aggarwal, CC, Srivastava, J & Yu, PS 2013, Community detection with prior knowledge. in Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. Siam Society, pp. 405-413, SIAM International Conference on Data Mining, SDM 2013, Austin, United States, 2/5/13.
Subbian K, Aggarwal CC, Srivastava J, Yu PS. Community detection with prior knowledge. In Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. Siam Society. 2013. p. 405-413
Subbian, Karthik ; Aggarwal, Charu C. ; Srivastava, Jaideep ; Yu, Philip S. / Community detection with prior knowledge. Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. Siam Society, 2013. pp. 405-413
@inproceedings{6a6eacfb672d41eebf154944cdd7d856,
title = "Community detection with prior knowledge",
abstract = "The problem of community detection is a challenging one because of the presence of hubs and noisy links, which tend to create highly imbalanced graph clusters. Often, these resulting clusters are not very intuitive and difficult to interpret. With the growing availability of network information, there is a significant amount of prior knowledge available about the communities in social, communication and several other networks. These community labels may be noisy and very limited, though they do help in community detection. In this paper, we explore the use of such noisy labeled information for finding high quality communities. We will present an adaptive density-based clustering which allows flexible incorporation of prior knowledge in to the community detection process. We use a random walk framework to compute the node densities and the level of supervision regulates the node densities and the quality of resulting density based clusters. Our framework is general enough to produce both overlapping and non-overlapping clusters. We empirically show that even with a tiny amount of supervision, our approach can produce superior communities compared to popular baselines.",
keywords = "Clusters, Communities, Supervision",
author = "Karthik Subbian and Aggarwal, {Charu C.} and Jaideep Srivastava and Yu, {Philip S.}",
year = "2013",
language = "English",
isbn = "9781611972627",
pages = "405--413",
booktitle = "Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013",
publisher = "Siam Society",

}

TY - GEN

T1 - Community detection with prior knowledge

AU - Subbian, Karthik

AU - Aggarwal, Charu C.

AU - Srivastava, Jaideep

AU - Yu, Philip S.

PY - 2013

Y1 - 2013

N2 - The problem of community detection is a challenging one because of the presence of hubs and noisy links, which tend to create highly imbalanced graph clusters. Often, these resulting clusters are not very intuitive and difficult to interpret. With the growing availability of network information, there is a significant amount of prior knowledge available about the communities in social, communication and several other networks. These community labels may be noisy and very limited, though they do help in community detection. In this paper, we explore the use of such noisy labeled information for finding high quality communities. We will present an adaptive density-based clustering which allows flexible incorporation of prior knowledge in to the community detection process. We use a random walk framework to compute the node densities and the level of supervision regulates the node densities and the quality of resulting density based clusters. Our framework is general enough to produce both overlapping and non-overlapping clusters. We empirically show that even with a tiny amount of supervision, our approach can produce superior communities compared to popular baselines.

AB - The problem of community detection is a challenging one because of the presence of hubs and noisy links, which tend to create highly imbalanced graph clusters. Often, these resulting clusters are not very intuitive and difficult to interpret. With the growing availability of network information, there is a significant amount of prior knowledge available about the communities in social, communication and several other networks. These community labels may be noisy and very limited, though they do help in community detection. In this paper, we explore the use of such noisy labeled information for finding high quality communities. We will present an adaptive density-based clustering which allows flexible incorporation of prior knowledge in to the community detection process. We use a random walk framework to compute the node densities and the level of supervision regulates the node densities and the quality of resulting density based clusters. Our framework is general enough to produce both overlapping and non-overlapping clusters. We empirically show that even with a tiny amount of supervision, our approach can produce superior communities compared to popular baselines.

KW - Clusters

KW - Communities

KW - Supervision

UR - http://www.scopus.com/inward/record.url?scp=84937601996&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84937601996&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781611972627

SP - 405

EP - 413

BT - Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013

PB - Siam Society

ER -