Community detection is the most common and growing area of interest in social and real-time network applications. In recent years, several community detection methods have been developed. Particularly, community detection in Local expansion methods have been proved as effective and efficiently. However, there are some fundamental issues to uncover the overlapping communities. The maximum methods are sensitive to enable the seeds initialization and construct the parameters, while others are insufficient to establish the pervasive overlaps. In this paper, we proposed the new unsupervised Map Reduce based local expansion method for uncovering overlapping communities depends seed nodes. The goal of the proposed method is to locate the leader nodes (seed nodes) of communities with the basic graph measures such as degree, betweenness and closeness centralities and then derive the communities based on the leader nodes. We proposed Map-Reduce based Fuzzy C- Means Clustering Algorithm to derive the overlapping communities based on leader nodes. We tested our proposed method Leader based Community Detection (LBCD) on the real-world data sets of totals of 11 and the experimental results shows the more effective and optimistic in terms of network graph enabled overlapping community structures evaluation.

[1] Apache Hadoop Manual. Available online: https://hadoop.apache.org/docs/r2.8.0/api/org/apa che/hadoop/util/ToolRunner.html, Last Visited, 2023.

[2] Ahn Y., Bagrow J., and Lehmann S., “Link Communities Reveal Multiscale Complexity in Networks,” Nature, vol. 466, no. 7307, pp. 761- 764, 2010. https://doi.org/10.1038/nature09182

[3] Dangalchev C., “Residual Closeness of Generalized Thorn Graphs,” Fundamenta Informaticae, vol. 162, no. 1, pp. 1-15, 2018. DOI: 10.3233/FI-2018-1710

[4] Filippone M., Camastra F., Masulli F., and Rovetta S., “A Survey of Kernel and Spectral Methods for Clustering,” Pattern Recognition, vol. 41, no. 1, pp. 176-190, 2008. https://doi.org/10.1016/j.patcog.2007.05.018

[5] Fortunato S., “Community Detection in Graphs,” Physics Reports, vol. 486, no. 3, pp. 75-174, 2010. https://doi.org/10.1016/j.physrep.2009.11.002

[6] Freeman L., Borgatti S., and White D., “Centrality in Valued Graphs: A Measure of Betweenness Based on Network Flow,” Social Networks, vol. 13, no. 2, pp. 141-154, 1991. https://doi.org/10.1016/0378-8733(91)90017-N

[7] Gregory S., “Finding Overlapping Communities in Networks by Label Propagation,” New Journal of Physics, vol. 12, no. 10, 2010. DOI:10.1088/1367-2630/12/10/103018

[8] Gupta S. and Singh D., “Seed Community Identification Framework for Community Detection over Social Media,” Arabian Journal of Science and Engineering, vol. 48, pp. 1829-1843, 2023. https://doi.org/10.1007/s13369-022-07020- z

[9] Giraph API Manual. Available online: (https://giraph.apache.org/, Last Visited, 2023. 862 The International Arab Journal of Information Technology, Vol. 20, No. 6, November 2023

[10] Khang T., Vuong N., Tran M., and Fowler M., “Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients,” Algorithms, vol. 13, no. 7, pp. 158, 2020. https://doi.org/10.3390/a13070158

[11] Lancichinetti A. and Fortunato S., “Limits of Modularity Maximization in Community Detection,” Physics Review E, vol. 84, no. 6, pp. 1-8, 2011. https://doi.org/10.48550/arXiv.1107.1155

[12] Marwa M., Saber B., Laid K., and Okba K., “A Personalized Recommendation for Web API,” The International Arab Journal of Information Technology, vol. 8, no. 3A, pp. 58-65, 2021. 2021 https://doi.org/10.34028/iajit/18/3A/7

[13] Mohamed M. and Latha K., “An Effective Community based Link Prediction Model for Improving Accuracy in Social Networks,” Journal of Intelligent and Fuzzy Systems, vol. 42, no. 3, pp. 2695-2711, 2022. 10.3233/JIFS-211821

[14] Newman M. and Girvan M., “Finding and Evaluating Community Structure in Networks,” Physical Review E, vol. 69, no. 2, 2004. https://doi.org/10.48550/arXiv.cond- mat/0308217

[15] Newman M., “Communities, Modules and Large- Scale Structure in Networks,” Nature Physics, vol. 8, no. 1, pp. 25-31, 2012. https://doi.org/10.1038/nphys2162

[16] Palla G., Derenyi I., Farkas I., and Vicsek T., “Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society,” Nature, vol. 435, no. 7043, pp. 814-818, 2005. https://doi.org/10.1038/nature03607

[17] Raghavan U., Albert R., and Kumara S., “Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks,” Physics Review E, vol. 76, no. 3, 2007. https://doi.org/10.48550/arXiv.0709.2938

[18] Rusinowska A., Berghammer R., De Swart H., and Grabisch M., “Social Networks: Prestige, Centrality, and Influence,” in Proceedings of the International Conference on Relational and Algebraic Methods in Computer Science, Rotterdam, pp. 22-39, 2011. https://doi.org/10.1007/978-3-642-21070-9_2

[19] Schaeffer S., “Graph Clustering,” Computer Science Review, vol. 1, no. 1, pp. 27-64, 2007. https://doi.org/10.1016/j.cosrev.2007.05.001

[20] Sun P., Gao L., and Yang Y., “Maximizing Modularity Intensity for Community Partition and Evolution,” Information Sciences, vol. 236, pp. 83-92, 2013. https://doi.org/10.1016/j.ins.2013.02.032

[21] Xie J. and Szymanski B., “Towards Linear Time Overlapping Community Detection in Social Networks,” in Proceedings of the Pacific-Asia Conference on Knowledge Discovery Data Mining, Kuala Lumpur, pp. 25-36, 2012. https://doi.org/10.48550/arXiv.1202.2465