Abstract
The widespread domain of social network analysis leads to numerous research challenges associated with it. Community detection is one of the foremost research challenges. There are several community detection methods available in literature whose effectiveness for detecting communities has been analysed through evaluation of various metrics. But this criteria of empirical analysis for predicting performance of particular community detection method, needs to be further explored and refined. Major challenge with earlier surveys on empirical analysis of overlapping community detection methods is the lack of multidimensional framework for depicting the results. In literature, majority of analysis have been done by considering performance metrics only. Unlike other empirical analysis represented in literature, this paper emphasizes on analysis of interdependencies among various fitness metrics while detecting communities. Co-performance analysis based on partition comparison of overlapping community detection methods is also presented. The evaluation has been performed on real as well as benchmark datasets. This article can serve as a reference work for researchers in selection of particular overlapping community detection algorithm based on the analysis of partition comparison and inter-dependencies among fitness metrics.
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References
Amelio A, Pizzuti C (2014) Overlapping community discovery methods: a survey. In: Social networks: analysis and case studies. Springer, pp 105–125
Arnaboldi V, Passarella A, Conti M, Dunbar RIM (2015) The structure of ego networks in Twitter. Online social networks, pp 61–73
Bedi P, Sharma C (2016) Community detection in social networks. Wiley Interdiscip Rev Data Min Knowl Discov 6(3):115–135
Camacho D, Panizo-LLedot A, Bello-Orgaz G, Gonzalez-Pardo A, Cambria E (2020) The four dimensions of social network analysis: an overview of research methods, applications, and software tools. Inf Fusion 63:88–120
Corradini E, Nocera A, Ursino D, Virgili L (2020) Defining and detecting k-bridges in a social network: the yelp case, and more. Knowl-Based Syst 195:1–22
Dao VL, Bothorel C, Lenca P (2020) Community structure: a comparative evaluation of community detection methods. Netw Sci 8(1):1–41
Devi JC, Poovammal E (2016) An analysis of overlapping community detection algorithms in social networks. Procedia Comput Sci 89:349–358
Du N, Wu B, Pei X, Wang B, Xu L (2007) Community detection in large-scale social networks. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on web mining and social network analysis, pp 16–25
Harenberg S, Bello G, Gjeltema L, Ranshous S, Harlalka J, Seay R, Padmanabhan K, Samatova N (2014) Community detection in large-scale networks: a survey and empirical evaluation. Wiley Interdiscip Rev Comput Stat 6(6):426–439
Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):046110
Lancichinetti A, Fortunato S, Kertész J (2009) Detecting the overlapping and hierarchical community structure in complex networks. New J Phys 11(3):033015
Leskovec J, Lang KJ, Mahoney M (2010) Empirical comparison of algorithms for network community detection. In: Proceedings of the 19th international conference on world wide web, pp 631–640
Luo L, Liu K, Guo B, Ma J (2020) User interaction-oriented community detection based on cascading analysis. Inf Sci 510:70–88
Mahabadi A, Hosseini M (2021) SLPA-based parallel overlapping community detection approach in large complex social networks. Multimed Tools Appl 80:6567–6598
Marquez R, Weber R (2019) Overlapping community detection in static and dynamic social networks. In: Proceedings of the twelfth ACM international conference on web search and data mining, pp 822–823
McCarthy AD, Chen T, Rudinger R, Matula DW (2019) Metrics matter in community detection. In: International conference on complex networks and their applications. Springer, pp 164–175
Muller E, Peres R (2019) The effect of social networks structure on innovation performance: a review and directions for research. Int J Res Mark 36(1):3–19
Murray G, Carenini G, Ng R (2012) Using the omega index for evaluating abstractive community detection. In: Proceedings of workshop on evaluation metrics and system comparison for automatic summarization, pp 10–18
Nagaratna M, Lakshmi S (2014) Benchmarks for overlapping community detection algorithm. Int J Sci Res 3(8):1690–1693
Nur N, Dou W, Niu X, Krishnan S, Park N (2018) Gi-ohms: graphical inference to detect overlapping communities. arXiv preprint arXiv:181001547
Palla G, Derenyi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818
Pizzuti C, Rombo SE (2014) Algorithms and tools for protein–protein interaction networks clustering, with a special focus on population-based stochastic methods. Bioinformatics 30(10):1343–1352
Python package (2020) https://pypi.org/project/networkx/
Rashmi C, Kodabagi M (2017) A review on overlapping community detection methodologies. In: International Conference on Smart Technologies for Smart Nation (SmartTechCon). IEEE, pp 1296–1300
Repository (2013) http://www-personal.umich.edu/~mejn/netdata/
Rossetti G, Pappalardo L, Rinzivillo S (2016) A novel approach to evaluate community detection algorithms on ground truth. In: Cherifi H, Gonçalves B, Menezes R, Sinatra R (eds) Complex networks VII. Studies in computational intelligence, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-30569-1_10
Shelke S, Attar V (2019) Source detection of rumor in social network–a review. Online Soc Netw Media 9:30–42
Sun Z, Wang B, Sheng J, Yu Z, Shao J (2018) Overlapping community detection based on information dynamics. IEEE Access 6:70919–70934
Whang JJ, Gleich DF, Dhillon IS (2016) Overlapping community detection using neighborhood-inflated seed expansion. IEEE Trans Knowl Data Eng 28(5):1272–1284
Xie J, Kelley S, Szymanski BK (2013) Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput Surv 45(4):1–3
Yang J, Leskovec J (2015) Defining and evaluating network communities based on ground-truth. Knowl Inf Syst 42(1):181–213
Yang Z, Algesheimer R, Tessone CJ (2016) A comparative analysis of community detection algorithms on artificial networks. Sci Rep 6:30750. https://doi.org/10.1038/srep30750
Yang G, Zheng W, Che C, Wang W (2019) Graph-based label propagation algorithm for community detection. Int J Mach Learn Cybern 11:1319–1329
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Saini, M., Mangat, V. Multidimensional empirical analysis of overlapping community detection methods in social networks. Multimed Tools Appl 82, 44655–44671 (2023). https://doi.org/10.1007/s11042-023-15489-5
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DOI: https://doi.org/10.1007/s11042-023-15489-5