research-article

On Integrating Network and Community Discovery

Authors:

Charu Aggarwal,

Jiawei HanAuthors Info & Claims

WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

Pages 117 - 126

https://doi.org/10.1145/2684822.2685323

Published: 02 February 2015 Publication History

Abstract

The problem of community detection has recently been studied widely in the context of the web and social media networks. Most algorithms for community detection assume that the entire network is available for online analysis. In practice, this is not really true, because only restricted portions of the network may be available at any given time for analysis. Many social networks such as Facebook have privacy constraints, which do not allow the discovery of the entire structure of the social network. Even in the case of more open networks such as Twitter, it may often be challenging to crawl the entire network from a practical perspective. For many other scenarios such as adversarial networks, the discovery of the entire network may itself be a costly task, and only a small portion of the network may be discovered at any given time. Therefore, it can be useful to investigate whether network mining algorithms can integrate the network discovery process tightly into the mining process, so that the best results are achieved for particular constraints on discovery costs. In this context, we will discuss algorithms for integrating community detection with network discovery. We will tightly integrate with the cost of actually discovering a network with the community detection process, so that the two processes can support each other and are performed in a mutually cohesive way. We present experimental results illustrating the advantages of the approach.

References

[1]

L. Backstrom, D. P. Huttenlocher, J. M. Kleinberg, and X. Lan, Groupformation in large social networks: membership, growth, and evolution, ACM KDD Conference, pp. 44--54, 2006.

Digital Library

[2]

B. Ball, B. Karrer and M. E. J. Newman, An efficient and principledmethod for detecting communities in networks, Physical Review, E84(3), 036103, 2011.

[3]

. Baykan, M. Henzinger, S. Keller, S. De Castelberg, and M.Kinzler, A comparison of techniques for sampling web pages, CoRR, abs/0902.1604, 2009. http://arxiv.org/abs/0902.1604

[4]

U. Brandes, M. Gaertler and D. Wagner, Experiments on graphclustering algorithms, European Symposium on Algorithms, pp.568--579, 2003.

[5]

C. Aggarwal and H. Wang, Managing and Mining Graph Data,Springer, 2010.

Digital Library

[6]

C. Aggarwal, Social Network Data Analytics, Springer, 2011.

[7]

D. Chakrabarti, R. Kumar, and A. Tomkins, Evolutionary clustering, ACM KDD Conference, pp. 554--560, 2006.

Digital Library

[8]

Y. Chi, X. Song, D. Zhou, K. Hino, and B. L. Tseng, Evolutionaryspectral clustering by incorporating temporal smoothness, ACM KDD Conference, pp. 153--162, 2007.

Digital Library

[9]

A. Clauset, M. E. J. Newman, and C. Moore, Finding communitystructure in very large networks, Physical Review, E 70(6),066111, 2004.

[10]

S. Fortunato, Community detection in graphs, PhysicsReports, 486(3), pp. 75--174, 2010.

[11]

D. Gibson, R. Kumar, and A. Tomkins, Discovering large densesubgraphs in massive graphs, VLDB Conference, pp. 721--732,2005.

Digital Library

[12]

. Gjoka, M. Kurant, C. T. Butts, and A. Markopoulou, Walking inFacebook: A Case Study of Unbiased Sampling of OSNs, IEEEINFOCOM Conference, pp. 1--9, 2010.

Digital Library

[13]

. Gjoka, C. T. Butts, M. Kurant, and A. Markopoulou, MultigraphSampling of Online Social Networks, IEEE Journal onMeasurement of Internet Topologies, 29(9), pp. 1893--1905, October2011.

[14]

. R. Henzinger, A. Heydon, M. Mitzenmacher, and M. Najork, Onnear-uniform URL sampling, WWW Conference, pp. 295--308,2000.

Digital Library

[15]

R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins, Trawlingthe web for emerging cyber-communities, WWW Conference, pp.1481--1493, 1999.

Digital Library

[16]

. Leskovec and C. Faloutsos, Sampling from largegraphs, ACM KDD Conference, pp. 631--636, 2006.

Digital Library

[17]

. Lin, X. Kong, P. Yu, Q. Wu, Y. Jia, andC. Li, Community detection in incomplete information networks, WWW Conference, pp. 341--350, 2012.

Digital Library

[18]

Y.-R. Lin, J. Sun, P. Castro, R. B. Konuru, H. Sundaram, andA. Kelliher, Extracting community structure through relationalhypergraphs, WWW Conference, pp. 1213--1214, 2009.

Digital Library

[19]

A. Y. Ng, M. Jordan, and Y. Weiss, On spectral clustering: Analysisand an algorithm, NIPS Conference, pp. 849--856, 2001.

[20]

. Qi, C. Aggarwal, and T. Huang, Communitydetection with edge content in social media networks, IEEE ICDEConference, pp. 534--545, 2012.

Digital Library

[21]

. Ribeiro and D. Towsley, Estimating and sampling graphs withmultidimensional random walks, ACM SIGCOMM Conference, pp.390--403, 2010.

Digital Library

[22]

S. Schaeffer, Graph Clustering, Computer Science Review,1(1), pp. 27--64, 2007.

Digital Library

[23]

J. Shi and J. Malik, Normalized Cuts and Image Segmentation, IEEE Transactions on Pattern Analysis and Intelligence, 22(8),pp. 888--905, 2000.

Digital Library

[24]

T. Yang, R. Jin, Y. Chi, and S. Zhu, Combining link and content for community detection: a discriminative approach, ACM KDD Conference, pp. 927--936, 2009.

Digital Library

[25]

. Ye, J. Lang, and F. Wu, Crawling online social graphs, Asia-Pacific Web Conference, pp. 236--242, 2010.

Digital Library

[26]

Z. Zeng, J. Wang, L. Zhou, and G. Karypis, Out-of-core coherentclosed quasi-clique mining from large dense graph databases, ACM TODS Journal, 31(2), 2007.

Digital Library

[27]

Y. Zhou, H. Cheng, and J. X. Yu, Graph clustering based onstructural/attribute similarities, VLDB Conference, pp. 718--729, 2009.

Digital Library

[28]

S. Zhang and H. Zhao, Community identification in networks withunbalanced structure, Physical Review, E 85(6), 066114, 2012.

[29]

S. Fortunato and M. Barthelemy, Resolution limit in community detection, Proceedings of the National Academy of Sciences, 104(1), pp. 36--41, 2007.

Cited By

Gasparetti FSansonetti GMicarelli A(2021)Community detection in social recommender systems: a surveyApplied Intelligence10.1007/s10489-020-01962-351:6(3975-3995)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s10489-020-01962-3
Wu ZWu Z(2020)An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive WeightsIEEE Access10.1109/ACCESS.2020.29723338(31171-31179)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2972333
Cui ZPark NChakraborty T(2020)Incremental community discovery via latent network representation and probabilistic inferenceKnowledge and Information Systems10.1007/s10115-019-01422-662:6(2281-2300)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1007/s10115-019-01422-6
Show More Cited By

Index Terms

On Integrating Network and Community Discovery
1. Information systems
  1. Information systems applications

Recommendations

Community detection for emerging social networks

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. ...
Enhancing community detection using a network weighting strategy

A community within a network is a group of vertices densely connected to each other but less connected to the vertices outside. The problem of detecting communities in large networks plays a key role in a wide range of research areas, e.g. Computer ...
ATM Network Discovery Using Mobile Agents
LCN '99: Proceedings of the 24th Annual IEEE Conference on Local Computer Networks

With the proliferation of new networking technologies, and the demand for new services, it will become increasingly difficult, and nearly impossible, to manage large-scale networks without an intelligent, automated management system. Discovering and ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

February 2015

482 pages

ISBN:9781450333177

DOI:10.1145/2684822

General Chairs:
Xueqi Cheng
ICT, Chinese Academy of Sciences, China
,
Hang Li
Huawei Technologies, China
,
Program Chairs:
Evgeniy Gabrilovich
Google, USA
,
Jie Tang
Tsinghua University, China

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 February 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Conference

WSDM 2015

Sponsor:

WSDM 2015: Eighth ACM International Conference on Web Search and Data Mining

February 2 - 6, 2015

Shanghai, China

Acceptance Rates

WSDM '15 Paper Acceptance Rate 39 of 238 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
492
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)1

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gasparetti FSansonetti GMicarelli A(2021)Community detection in social recommender systems: a surveyApplied Intelligence10.1007/s10489-020-01962-351:6(3975-3995)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s10489-020-01962-3
Wu ZWu Z(2020)An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive WeightsIEEE Access10.1109/ACCESS.2020.29723338(31171-31179)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2972333
Cui ZPark NChakraborty T(2020)Incremental community discovery via latent network representation and probabilistic inferenceKnowledge and Information Systems10.1007/s10115-019-01422-662:6(2281-2300)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1007/s10115-019-01422-6
Wang HWang FXu KWang HWang FXu K(2020)Clustering of Online Social Network GraphsModeling Information Diffusion in Online Social Networks with Partial Differential Equations10.1007/978-3-030-38852-2_4(27-41)Online publication date: 17-Mar-2020
https://doi.org/10.1007/978-3-030-38852-2_4
Wang HWang FXu KWang HWang FXu K(2020)IntroductionModeling Information Diffusion in Online Social Networks with Partial Differential Equations10.1007/978-3-030-38852-2_1(1-2)Online publication date: 17-Mar-2020
https://doi.org/10.1007/978-3-030-38852-2_1
Wu ZWang B(2019)DwfwFcm: An effective fuzzy c-means clustering framework considering the different data weights and feature weightsJournal of Intelligent & Fuzzy Systems10.3233/JIFS-19044037:3(4339-4347)Online publication date: 9-Oct-2019
https://doi.org/10.3233/JIFS-190440
Deng XNie ZZhai J(2019)Probabilistic Graphical Model Based Highly Scalable Directed Community Detection AlgorithmTrends and Applications in Knowledge Discovery and Data Mining10.1007/978-3-030-26142-9_28(322-340)Online publication date: 14-Apr-2019
https://dl.acm.org/doi/10.1007/978-3-030-26142-9_28
Sarkar SKarn AMukherjee ABhowmick S(2019)An Empirical Study of the Effect of Noise Models on Centrality MetricsDynamics On and Of Complex Networks III10.1007/978-3-030-14683-2_1(3-21)Online publication date: 14-May-2019
https://doi.org/10.1007/978-3-030-14683-2_1
Sarkar SKumar SBhowmick SMukherjee A(2018)Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness, and ReliabilityMachine Learning Techniques for Online Social Networks10.1007/978-3-319-89932-9_7(135-154)Online publication date: 31-May-2018
https://doi.org/10.1007/978-3-319-89932-9_7
Duong VKhan KLee Y(2017)Top-k frequent induced subgraph mining on a sliding window using samplingProceedings of the 11th International Conference on Ubiquitous Information Management and Communication10.1145/3022227.3022242(1-7)Online publication date: 5-Jan-2017
https://dl.acm.org/doi/10.1145/3022227.3022242
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten