Shanjidah Akhter
ABSTRACT
In today's online social network systems, various communities comprising various unique characteristics and interests play a significant role in complex network analysis. Often such communities comprise nodes which belong to many different communities, resulting rise of parallel overlapping communities. To effectively detect such parallel overlapping communities, various algorithms have been proposed based on node clustering, edge clustering and other approaches. In this paper, these algorithms to detect parallel overlapping communities are studied thoroughly, and their performances are analyzed comparatively. Graph-based approach such as Sequencial Clique Percolation (SCP), intrinsic Longitudinal Community Detection (iLCD), link-based approach such as Top Graph Clusters (TopGC), fuzzy-based approach such as Cluster-Overlap Newman Girvan Algorithm (CONGA), agent and dynamic-based and modularity scoring based approach etc. are comparatively analyzed to evaluate their performance for overlapping community detection using dataset from Zachary karate club and Stanford large network. Analysis conducted on SCP and iLCD using the same datasets show that SCP cannot detect dynamic overlapping communities in a dense network as effectively as iLCD. It is also observed, CONGA can detect the distinctive community clusters in Zachary karate dataset. It is also noted that TopGC is suitable in detecting overlapping clusters from large networks with limited time and memory usage, making it useful for experiments in workstations with lower specs and limited processing power.
- J. C. Devi, E. Poovammall. 2016. An analysis of overlapping community detection algorithms in social networks. Procedia Computer Science Journal, 89, 349-358. DOI: 10.1016/j.procs.2016.06.082.Google Scholar
Cross Ref
- J. Li, X. Wang, J. Eustace. 2013. Detecting overlapping communities by seed community in weighted complex networks. Physica A: Statistical Mechanics and its Applications, 392, 23, 6125-6134. DOI: 10.1016/j.physa.2013.07.066.Google Scholar
- C. Shi, Y. Cai, D. Fu, Y. Dong, B. Wu. 2013. A link clustering based overlapping community detection algorithm. Data & Knowledge Engineering, 87, 394-404. DOI: 10.1016/j.datak.2013.05.004.Google ScholarDigital Library
- J. Zhang, S. Ge. 2012. A parallel algorithm to find overlapping community structure in directed and weighted complex networks. In 2nd International Conference on Instrumentation and Measurement, Computer, Communication and Control, 1561–1564. DOI: 10.1109/IMCCC.2012.364.Google ScholarDigital Library
- Y. Zhang, J. Wang, Y. Wang, L. Zhou. 2009. Parallel community detection on large networks with propinquity dynamics. In KDD '09, 997-1006. DOI: 10.1145/1557019.1557127.Google ScholarDigital Library
- W. Zhi-Xiao 2016. Overlapping community detection based on node location analysis. Knowledge-Based Systems, 105, 225-235. DOI: 10.1016/j.knosys.2016.05.024.Google ScholarDigital Library
- N. Nguyen, T. Dinh, D. Nguyen, M. Thai. 2011. Overlapping community structures and their detection on social networks. In IEEE Third International Conference on Privacy Security Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 35-40. DOI: 10.1109/PASSAT/SocialCom.2011.16.Google ScholarCross Ref
- R. Li, W. Guo, K. Guo, Q. Qiu. 2015. Parallel Multi-label Propagation for Overlapping Community Detection in Large-Scale Networks. In Bikakis A., Zheng X. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI, 2015. 9426, 351-362. DOI: 10.1007/978-3-319-26181-2_33.Google Scholar
- Q. Zhang, Q. Qirong, K. Guo. 2015. Parallel overlapping community discovery based on grey relational analysis. In IEEE International Conference on Grey Systems and Intelligent Services (GSIS), 151-156. DOI: 10.1109/GSIS.2015.7301846.Google Scholar
- K. Kuzmin, S. Y. Shah, B. K. Szymanski. 2013. Parallel Overlapping Community Detection with SLPA. In 2013 International Conference on Social Computing, 204-212. DOI: 10.1109/SocialCom.2013.37.Google ScholarDigital Library
- Y. Qiao, H. Wang, D. Wang. 2017. Parallelizing and Optimizing Overlapping Community Detection with Speaker-Listener Label Propagation Algorithm on Multi-Core Architecture. In IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 439-443. DOI: 10.1109/ICCCBDA.2017.7951954.Google Scholar
- Z. Lu, X. Sun, Y. Wen, G. Cao, T. La Porta. 2015. Algorithms and applications for community detection in weighted networks. In IEEE Transactions on Parallel and Distributed Systems, 26, 11, 2916-2926. DOI: 10.1109/TPDS.2014.2370031.Google ScholarDigital Library
- Zachary karate club, http://konect.cc/networks/ucidata-zachary/, accessed on February 15, 2022.Google Scholar
- Stanford Large Network Dataset Collection, http://snap.stanford.edu/data/#socnets, accessed on February 15, 2022.Google Scholar
- GitHub repository, https://github.com/GraphProcessor/CommunityDetectionCodes/blob/master/Survey/Overlapping-Community-Detection-Codes.md#fuzzy-detection, accessed on February 15, 2022.Google Scholar
- J. M. Kumpula, M. Kivelä, K. Kaski, J. Saramäki. 2008. A sequential algorithm for fast clique percolation. Physical Review E, 78, 8. DOI: 10.1103/PhysRevE.78.026109.Google ScholarCross Ref
- IceLib, https://help.genesys.com/developer/cic/docs/icelib/webhelp/index.html, accessed on February 15, 2022.Google Scholar
- R. Cazabet, F. Amblard, C. Hanachi. 2010. Detection of overlapping communities in dynamical social networks. In IEEE Second International Conference on Social Computing, 309-314. DOI: 10.1109/SocialCom.2010.51.Google ScholarDigital Library
- K. Nath, S. Roy, S. Nandi. 2021. An incremental approach for hierarchical community mining in evolving social graphs. International Journal of Intelligent Enterprise, 8, 2-3, 123-141.Google ScholarCross Ref
- N. Alotaibia, D. Rhouma. 2021. A review on community structures detection in time evolving social networks. Journal of King Saud University - Computer and Information Sciences. DOI: 10.1016/j.jksuci.2021.08.016.Google Scholar
- S. Souravlas 2021. A classification of community detection methods in social networks: a survey. International Journal of General Systems, 50, 1, 63-91, DOI: 10.1080/03081079.2020.1863394.Google ScholarCross Ref
- Performance Analysis of Parallel Overlapping Community Detection Algorithms in Large-scale Social Networks
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