Cornmunity detection with prior knowledge

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

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

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 publicationSIAM International Conference on Data Mining 2013, SMD 2013
PublisherSociety for Industrial and Applied Mathematics Publications
Pages405-413
Number of pages9
ISBN (Print)9781627487245
Publication statusPublished - 2013
Externally publishedYes
Event13th SIAM International Conference on Data Mining, SMD 2013 - Austin, United States
Duration: 2 May 20134 May 2013

Other

Other13th SIAM International Conference on Data Mining, SMD 2013
CountryUnited States
CityAustin
Period2/5/134/5/13

Fingerprint

Prior Knowledge
Community Detection
Labels
Availability
Communication
Vertex of a graph
Overlapping
Intuitive
Baseline
Random walk
Clustering
Tend
Community
Graph in graph theory
Framework

Keywords

  • Clusters
  • Communities
  • Supervision

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Signal Processing
  • Software

Cite this

Subbian, K., Aggarwal, C. C., Srivastava, J., & Yu, P. S. (2013). Cornmunity detection with prior knowledge. In SIAM International Conference on Data Mining 2013, SMD 2013 (pp. 405-413). Society for Industrial and Applied Mathematics Publications.

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

SIAM International Conference on Data Mining 2013, SMD 2013. Society for Industrial and Applied Mathematics Publications, 2013. p. 405-413.

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

Subbian, K, Aggarwal, CC, Srivastava, J & Yu, PS 2013, Cornmunity detection with prior knowledge. in SIAM International Conference on Data Mining 2013, SMD 2013. Society for Industrial and Applied Mathematics Publications, pp. 405-413, 13th SIAM International Conference on Data Mining, SMD 2013, Austin, United States, 2/5/13.
Subbian K, Aggarwal CC, Srivastava J, Yu PS. Cornmunity detection with prior knowledge. In SIAM International Conference on Data Mining 2013, SMD 2013. Society for Industrial and Applied Mathematics Publications. 2013. p. 405-413
Subbian, Karthik ; Aggarwal, Charu C. ; Srivastava, Jaideep ; Yu, Philip S. / Cornmunity detection with prior knowledge. SIAM International Conference on Data Mining 2013, SMD 2013. Society for Industrial and Applied Mathematics Publications, 2013. pp. 405-413
@inproceedings{5b8862f4bef146c295e4fb93b42bbd7a,
title = "Cornmunity 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 = "9781627487245",
pages = "405--413",
booktitle = "SIAM International Conference on Data Mining 2013, SMD 2013",
publisher = "Society for Industrial and Applied Mathematics Publications",

}

TY - GEN

T1 - Cornmunity 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=84960475683&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84960475683&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781627487245

SP - 405

EP - 413

BT - SIAM International Conference on Data Mining 2013, SMD 2013

PB - Society for Industrial and Applied Mathematics Publications

ER -