skip to main content
10.1145/3127404.3127412acmotherconferencesArticle/Chapter ViewAbstractPublication PageschinesecscwConference Proceedingsconference-collections
research-article

Overlapping Community Detection Based on Random Walk and Seeds Extension

Published: 22 September 2017 Publication History

Abstract

As an important research direction in the complex social network, the difficulty of community detection lies in the search and discovery of social structures efficiently and accurately. In this study, an algorithm named SEOCD (Seeds Extension Overlapping Community Detection) for overlapping community detection based on random walk and seeds extension is proposed in order to solve the problem of seeds selection and expansion in many seed-based algorithms. First, SEOCD uses the random walk strategy to find the seed communities with tight structures. Second, from the seed communities, the similarity between each pair of node and community is calculated. The nodes whose similarity greater than a predefined threshold are selected. Third, The strategy of optimizing a self-adaptive function is used to expand the communities. Finally, The free nodes in the network are assigned to their corresponding communities, which finds out all the overlapping community structures. Experiments on real and artificial networks show that SEOCD is capable of discovering overlapping communities in complex social networks efficiently.

References

[1]
Mark E. Newman. 2001. The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences 98, 2 (2001), 404--409.
[2]
M. Faloutsos, P. Faloutsos, and C. Faloutsos. 1999. On power-law relationships of the internet topology. ACM SIGCOMM computer communication review. ACM 29, 4 (1999), 251--262.
[3]
M. Takaffoli, F. Sangi, J. Fagnan, and O. R. Zäıane. 2011. Community evolution mining in dynamic social networks. Procedia-Social and Behavioral Sciences 22, (2011), 49--58.
[4]
S. Fortunato. 2010. Community detection in graphs. Physics Reports 486, 3 (2010), 75--174.
[5]
M. Girvan and Mark E. Newman. 2002. Community structure in social and biological networks. Proceedings of the national academy of sciences 99, 12 (2002), 7821--7826.
[6]
F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi. 2004. Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America 101, 9 (2004), 2658--2663.
[7]
A. Clauset, Mark E. Newman, and C. Moore. 2004. Finding community structure in very large networks. Physical review E 70, 6 (2004), 066111.
[8]
U. Von Luxburg. 2007. A tutorial on spectral clustering. Statistics and computing 17, 4 (2007), 395--416.
[9]
L. Huang, R. Li, H. Chen, X. Gu, K. Wen, and Y. Li. 2014. Detecting network communities using regularized spectral clustering algorithm. Artificial Intelligence Review, (2014), 1--16.
[10]
G. Palla, I. Derényi, I. Farkas, and T. Vicsek. 2005. Uncovering the overlapping community structure of complex networks in nature and society. arXiv preprint physics/0506133 435, (2005), 814--818.
[11]
X. Wen, W. N. Chen, Y. Lin, T. Gu, H. Zhang, Y. Li, ... and J. Zhang. 2017. A maximal clique based multiobjective evolutionary algorithm for overlapping community detection. IEEE Transactions on Evolutionary Computation 21, 3 (2017), 363--377.
[12]
J. B. Pereira - Leal, A. J. Enright, and C. A. Ouzounis. 2004. Detection of functional modules from protein interaction networks. PROTEINS: Structure, Function, and Bioinformatics 54, 1 (2004), 49--57.
[13]
Y. Kim, H. Jeong. 2011. Map equation for link communities. Physical Review E 84, 2 (2011), 026110.
[14]
U. N. Raghavan, R. Albert, and S. Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks. Physical review E 76, 3 (2007), 036106.
[15]
S. Gregory. 2009. Finding overlapping communities in networks by label propagation. New Journal of Physics 12, 10 (2009), 2011--2024.
[16]
J. Xie and B. K. Szymanski. 2012. Towards linear time overlapping community detection in social networks. Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, (2012), 25--36.
[17]
A. Lancichinetti, S. Fortunato, and J. Kertész. 2009. Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics 11, 3 (2009), 033015.
[18]
M. Coscia, G. Rossetti, F. Giannotti, and D. Pedreschi. 2012. Demon: a local-first discovery method for overlapping communities. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, (2012), 615--623.
[19]
W. Zhao, F. Zhang, and J. Liu. 2016. Local Community Detection via Edge Weighting. Information Retrieval Technology. Springer International Publishing, (2016), 68--80.
[20]
J. Liu, D. Wang, S. Feng, Y. Zhang, and W. Zhao. 2016. A Novel Approach of Discovering Local Community Using Node Vector Model. In International Conference on Web Information Systems Engineering Springer, Cham, (2016), 513--521.
[21]
Y. Su, B. Wang, and X. Zhang. 2017. A seed-expanding method based on random walks for community detection in networks with ambiguous community structures. Scientific Reports, 7 (2017).
[22]
Q. Chen, T. T. Wu, and M. Fang. 2013. Detecting local community structures in complex networks based on local degree central nodes. Physica A: Statistical Mechanics and its Applications 392, 3 (2013), 529--537.
[23]
J. J. Whang, D. F. Gleich, and I. S. Dhillon. 2013. Overlapping community detection using seed set expansion. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, 10 (2013).
[24]
F. Moradi, T. Olovsson, and P. Tsigas. 2014. A local seed selection algorithm for overlapping community detection. Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on. IEEE, (2014), 1--8.
[25]
C. Su, Y. Wang, and L. Zhang. 2014. A New Method for Community Detection Using Seed Nodes. Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)-Volume 02. IEEE Computer Society, (2014), 429--435.
[26]
D. F. Gleich and C. Seshadhri. 2012. Vertex neighborhoods, low conductance cuts, and good seeds for local community methods. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, (2012), 597--605.
[27]
W. W. Zachary. 1977. An information flow model for conflict and fission in small groups. Journal of anthropological research 33, 4 (1977), 452--473.
[28]
D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson. 2003. The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations. Behavioral Ecology and Sociobiology 54, 4 (2003), 396--405.
[29]
Mark E. Newman and M. Girvan. 2004. Finding and evaluating community structure in networks. Physical review E 69, 2 (2004), 026113.
[30]
P. M. Gleiser and L. Danon. 2003. Community structure in jazz. Advances in complex systems 6, 4 (2003), 565--573.
[31]
A. Lancichinetti, S. Fortunato, and F. Radicchi. 2008. Benchmark graphs for testing community detection algorithms. Physical Review E Statistical Nonlinear & Soft Matter Physics 78, 2 (2008), 046110.
[32]
H. Shen, X. Cheng, K. Cai, and M. B. Hu. 2009. Detect overlapping and hierarchical community structure in networks. Physica A: Statistical Mechanics and its Applications 388, 8 (2009), 1706--1712.
[33]
L. Danon, A. Diaz-Guilera, J. Duch, and A. Arenas. 2005. Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 09 (2005), P09008.
[34]
J. Xie, S. Kelley, and B. K. Szymanski. 2013. Overlapping community detection in networks: The state-of-the-art and comparative study. Acm computing surveys (csur) 45, 4 (2013).

Cited By

View all
  • (2022)Density-Peak-Based Overlapping Community Detection AlgorithmIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.31220189:4(1211-1223)Online publication date: Aug-2022
  • (2020)Overlapping Community Detection Method Based on Network Representation Learning and Density PeaksIEEE Access10.1109/ACCESS.2020.30414728(226506-226514)Online publication date: 2020
  • (2019)Overlapping Community Detection Algorithm Based on Coarsening and Local Overlapping ModularityIEEE Access10.1109/ACCESS.2019.29121827(57943-57955)Online publication date: 2019
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ChineseCSCW '17: Proceedings of the 12th Chinese Conference on Computer Supported Cooperative Work and Social Computing
September 2017
269 pages
ISBN:9781450353526
DOI:10.1145/3127404
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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 September 2017

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Local community detection
  2. overlapping community
  3. random walk
  4. seeds extension

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ChineseCSCW '17

Acceptance Rates

ChineseCSCW '17 Paper Acceptance Rate 21 of 84 submissions, 25%;
Overall Acceptance Rate 21 of 84 submissions, 25%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)15
  • Downloads (Last 6 weeks)1
Reflects downloads up to 28 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2022)Density-Peak-Based Overlapping Community Detection AlgorithmIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.31220189:4(1211-1223)Online publication date: Aug-2022
  • (2020)Overlapping Community Detection Method Based on Network Representation Learning and Density PeaksIEEE Access10.1109/ACCESS.2020.30414728(226506-226514)Online publication date: 2020
  • (2019)Overlapping Community Detection Algorithm Based on Coarsening and Local Overlapping ModularityIEEE Access10.1109/ACCESS.2019.29121827(57943-57955)Online publication date: 2019
  • (2018)Dynamic Trustworthiness Overlapping Community Discovery in Mobile Internet of ThingsIEEE Access10.1109/ACCESS.2018.28840026(74579-74597)Online publication date: 2018

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media