Community Detection in Practice: A Review of Real-World Applications Across Six Themes

Abal Abas, Z., Norizan, M. N., Zainal Abidin, Z., Abdul Rahman, A. F. N., Rahmalan, H., Ahmed Tharbe, I. H., Wan Fakhruddin, W. F. W., Mohd Zaki, N. H., & Ahmad Sobri, S. (2022). Modeling Physical Interaction and Understanding Peer Group Learning Dynamics: Graph Analytics Approach Perspective. Mathematics, 10(9), 1430. https://doi.org/10.3390/math10091430
Abas, Z. A., Fadzli, A., Abdul, N., Pramudya, G., Wee, S. Y., Kasmin, F., Yusof, N., Abidin, Z. Z., & Jaya, H. T. (2020). Analytics?: A Review of Current Trends, Future Application and Challenges. 9(I).
Abbe, E., Baccelli, F., & Sankararaman, A. (2021). Community detection on Euclidean random graphs. Information and Inference: A Journal of the IMA, 10(1), 109–160. https://doi.org/10.1093/imaiai/iaaa009
Agapito, G., Milano, M., & Cannataro, M. (2022). Parallel Network Analysis and Communities Detection (PANC) Pipeline for the Analysis and Visualization of COVID-19 Data. Parallel Processing Letters, 32(01n02). https://doi.org/10.1142/S0129626421420020
Ahmadian, S., Rostami, M., Jalali, S. M. J., Oussalah, M., & Farrahi, V. (2022). Healthy Food Recommendation Using a Time-Aware Community Detection Approach and Reliability Measurement. International Journal of Computational Intelligence Systems, 15(1), 105. https://doi.org/10.1007/s44196-022-00168-4
Akbar, S., & Saritha, S. K. (2021). Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change. Scientific Reports, 11(1), 14332. https://doi.org/10.1038/s41598-021-93122-x
Akbar, Z., Liu, J., & Latif, Z. (2021). Mining social applications network from business perspective using modularity maximization for community detection. Social Network Analysis and Mining. https://doi.org/10.1007/s13278-021-00798-0
Al-Mukhtar, A. F., & Al-shamery, E. S. (2019). Community detection of political blogs network based on structure-attribute graph clustering model. International Journal of Electrical and Computer Engineering (IJECE), 9(3), 2121. https://doi.org/10.11591/ijece.v9i3.pp2121-2130
Al-sharoa, E., Al-khassaweneh, M., & Aviyente, S. (2019). Tensor Based Temporal and Multilayer Community Detection for Studying Brain Dynamics During Resting State fMRI. IEEE Transactions on Biomedical Engineering, 66(3), 695–709. https://doi.org/10.1109/TBME.2018.2854676
Alcácer, V., & Cruz-Machado, V. (2019). Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems. Engineering Science and Technology, an International Journal, 22(3), 899–919. https://doi.org/10.1016/j.jestch.2019.01.006
Alcayde, A., G. Montoya, F., Baños, R., Perea-Moreno, A.-J., & Manzano-Agugliaro, F. (2018). Analysis of Research Topics and Scientific Collaborations in Renewable Energy Using Community Detection. Sustainability, 10(12), 4510. https://doi.org/10.3390/su10124510
Alduaiji, N., Datta, A., & Li, J. (2018). Influence Propagation Model for Clique-Based Community Detection in Social Networks. IEEE Transactions on Computational Social Systems, 5(2), 563–575. https://doi.org/10.1109/TCSS.2018.2831694
Basanta, C. D. L. A. C., Bazzi, M., Hijazi, M., Bessant, C., & Cutillas, P. R. (2023). Community detection in empirical kinase networks identifies new potential members of signalling pathways. PLOS Computational Biology, 19(6), e1010459. https://doi.org/10.1371/journal.pcbi.1010459
Belli, D., Chessa, S., Foschini, L., & Girolami, M. (2020). The rhythm of the crowd: Properties of evolutionary community detection algorithms for mobile edge selection. Pervasive and Mobile Computing, 67, 101231. https://doi.org/10.1016/j.pmcj.2020.101231
Bhar, A., Gierse, L. C., Meene, A., Wang, H., Karte, C., Schwaiger, T., Schröder, C., Mettenleiter, T. C., Urich, T., Riedel, K., & Kaderali, L. (2022). Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection. Frontiers in Microbiology, 13. https://doi.org/10.3389/fmicb.2022.979320
Bi, X., Qiu, T., Qu, W., Zhao, L., Zhou, X., & Wu, D. O. (2021). Dynamically Transient Social Community Detection for Mobile Social Networks. IEEE Internet of Things Journal, 8(3), 1282–1293. https://doi.org/10.1109/JIOT.2020.3001309
Bialek, W., Cavagna, A., Giardina, I., Mora, T., Silvestri, E., Viale, M., & Walczak, A. M. (2012). Statistical mechanics for natural flocks of birds. Proceedings of the National Academy of Sciences of the United States of America, 109(13), 4786–4791. https://doi.org/10.1073/pnas.1118633109
Cao, J., Liu, W., Cao, B., Wang, P., Li, S., Liu, B., & Iqbal, M. (2019). Social Relationships and Temp-Spatial Behaviors Based Community Discovery to Improve Cyber Security Practices. IEEE Access, 7, 105973–105986. https://doi.org/10.1109/ACCESS.2019.2931937
Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019). A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities. IEEE Communications Surveys and Tutorials, 21(3), 2419–2465. https://doi.org/10.1109/COMST.2019.2914030
Ceron, W., Santos, L. B. L. L., Neto, G. D., Quiles, M. G., & Candido, O. A. (2020). Community Detection in Very High-Resolution Meteorological Networks. IEEE Geoscience and Remote Sensing Letters, 17(11), 2007–2010. https://doi.org/10.1109/LGRS.2019.2955508
Chatterjee, S., & Sanjeev, B. S. (2023). Community detection in Epstein-Barr virus associated carcinomas and role of tyrosine kinase in etiological mechanisms for oncogenesis. Microbial Pathogenesis, 180(May), 106115. https://doi.org/10.1016/j.micpath.2023.106115
Chessa, A., D’Urso, P., De Giovanni, L., Vitale, V., & Gebbia, A. (2023). Complex networks for community detection of basketball players. Annals of Operations Research, 325(1), 363–389. https://doi.org/10.1007/s10479-022-04647-x
Chi, Y., Song, X., Zhou, D., Hino, K., & Tseng, B. L. (2007). Evolutionary spectral clustering by incorporating temporal smoothness. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 153–162. https://doi.org/10.1145/1281192.1281212
Chien, I. E., Lin, C.-Y. C. Y., & Wang, I.-H. H. (2019). On the Minimax Misclassification Ratio of Hypergraph Community Detection. IEEE Transactions on Information Theory, 65(12), 8095–8118. https://doi.org/10.1109/TIT.2019.2928301
Choudhary, C., Singh, I., & Kumar, M. (2023). Community detection algorithms for recommendation systems: techniques and metrics. Computing, 105(2), 417–453. https://doi.org/10.1007/s00607-022-01131-z
Choudhury, T., Arunachalam, R., Khanna, A., Jasinska, E., Bolshev, V., Panchenko, V., & Leonowicz, Z. (2022). A Social Network Analysis Approach to COVID-19 Community Detection Techniques. International Journal of Environmental Research and Public Health, 19(7). https://doi.org/10.3390/ijerph19073791
Chunaev, P. (2020). Community detection in node-attributed social networks: A survey. Computer Science Review, 37, 100286. https://doi.org/10.1016/j.cosrev.2020.100286
Clarke, V., & Braun, V. (2013). Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The Psychologist, 26(2), 120–123. http://eprints.uwe.ac.uk/21155/3/Teaching thematic analysis Research Repository version.pdf
Cotelo, J. M., Ortega, F. J., Troyano, J. A., Enriquez, F., & Cruz, F. L. (2020). Known by Who We Follow: A Biclustering Application to Community Detection. IEEE Access, 8, 192218–192228. https://doi.org/10.1109/ACCESS.2020.3032015
Daher, A., Coupechoux, M., Godlewski, P., Ngouat, P., & Minot, P. (2020). A Dynamic Clustering Algorithm for Multi-Point Transmissions in Mission-Critical Communications. IEEE Transactions on Wireless Communications, 19(7), 4934–4946. https://doi.org/10.1109/TWC.2020.2988382
Dakiche, N., Benbouzid-Si Tayeb, F., Slimani, Y., & Benatchba, K. (2019). Tracking community evolution in social networks: A survey. Information Processing & Management, 56(3), 1084–1102. https://doi.org/10.1016/j.ipm.2018.03.005
Damgacioglu, H., Celik, E., & Celik, N. (2020). Intra-Cluster Distance Minimization in DNA Methylation Analysis Using an Advanced Tabu-Based Iterative kk-Medoids Clustering Algorithm (T-CLUST). IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(4), 1241–1252. https://doi.org/10.1109/TCBB.2018.2886006
De Luca, M., Fasolino, A. R., Ferraro, A., Moscato, V., Sperlí, G., & Tramontana, P. (2023). A community detection approach based on network representation learning for repository mining. Expert Systems with Applications, 231(September 2022), 120597. https://doi.org/10.1016/j.eswa.2023.120597
Delmas, E., Besson, M., Brice, M. H., Burkle, L. A., Dalla Riva, G. V., Fortin, M. J., Gravel, D., Guimarães, P. R., Hembry, D. H., Newman, E. A., Olesen, J. M., Pires, M. M., Yeakel, J. D., & Poisot, T. (2019). Analysing ecological networks of species interactions. Biological Reviews, 94(1), 16–36. https://doi.org/10.1111/brv.12433
Desai, V. D., Fazelpour, F., Handwerger, A. L., & Daniels, K. E. (2023). Forecasting landslides using community detection on geophysical satellite data. Physical Review E, 108(1), 014901. https://doi.org/10.1103/PhysRevE.108.014901
Dey, A. K., Tian, Y., & Gel, Y. R. (2022). Community detection in complex networks: From statistical foundations to data science applications. WIREs Computational Statistics, 14(2), 1–27. https://doi.org/10.1002/wics.1566
Ding, R., Fu, J., Du, Y., Du, L., Zhou, T., Zhang, Y., Shen, S., Zhu, Y., & Chen, S. (2022). Structural Evolution and Community Detection of China Rail Transit Route Network. Sustainability, 14(19), 12342. https://doi.org/10.3390/su141912342
Doluca, O., & O?uz, K. (2021). APAL: Adjacency Propagation Algorithm for overlapping community detection in biological networks. Information Sciences, 579, 574–590. https://doi.org/10.1016/j.ins.2021.08.031
Elhishi, S., Abu-Elkheir, M., & Abou Elfetouh, A. (2019). Perspectives on the evolution of online communities. Behaviour & Information Technology, 38(6), 592–608. https://doi.org/10.1080/0144929X.2018.1546901
Essaid, M., & Ju, H. (2022). Deep Learning-Based Community Detection Approach on Bitcoin Network. Systems, 10(6), 203. https://doi.org/10.3390/systems10060203
Evans, J. C., Lindholm, A. K., & König, B. (2022). Family dynamics reveal that female house mice preferentially breed in their maternal community. Behavioral Ecology, 33(1), 222–232. https://doi.org/10.1093/beheco/arab128
Fernandes, A., Gonçalves, P. C. T., Campos, P., & Delgado, C. (2019a). Centrality and community detection: a co-marketing multilayer network. Journal of Business & Industrial Marketing, 34(8), 1749–1762. https://doi.org/10.1108/JBIM-11-2017-0266
Fernandes, A., Gonçalves, P. C. T. T., Campos, P., & Delgado, C. (2019b). Centrality and community detection: a co-marketing multilayer network. Journal of Business & Industrial Marketing, 34(8), 1749–1762. https://doi.org/10.1108/JBIM-11-2017-0266
Folino, F., & Pizzuti, C. (2014). An Evolutionary Multiobjective Approach for Community Discovery in Dynamic Networks. IEEE Transactions on Knowledge and Data Engineering, 26(8), 1838–1852. https://doi.org/10.1109/TKDE.2013.131
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3–5), 75–174. https://doi.org/10.1016/j.physrep.2009.11.002
Fortunato, S., & Hric, D. (2016). Community detection in networks: A user guide. Physics Reports, 659, 1–44. https://doi.org/10.1016/j.physrep.2016.09.002
Gaber, A., Abdelbaki, N., & Arafa, T. (2023). Community Detection Applications in Mobile Networks: Towards Data-Driven Design for Next Generation Cellular Networks. Journal of Communications and Information Networks, 8(2), 155–163. https://doi.org/10.23919/JCIN.2023.10173731
Gao, J. (2021). Tree structures are employed by pigeons as leadership structures to ock. 0–8. https://doi.org/10.21203/rs.3.rs-1000239/v1
Grant, W. P., & Ahnert, S. E. (2019). Modular decomposition of protein structure using community detection. Journal of Complex Networks, 7(1), 101–113. https://doi.org/10.1093/comnet/cny014
Grassi, R., Bartesaghi, P., Benati, S., & Clemente, G. P. (2021). Multi-Attribute Community Detection in International Trade Network. Networks and Spatial Economics, 21(3), 707–733. https://doi.org/10.1007/s11067-021-09547-4
Gu, K., Liu, D., & Wang, K. (2019). Social Community Detection Scheme Based on Social-Aware in Mobile Social Networks. IEEE Access, 7, 173407–173418. https://doi.org/10.1109/ACCESS.2019.2956149
Guo, Y., Yang, L., Hao, S., & Gao, J. (2019). Dynamic identification of urban traffic congestion warning communities in heterogeneous networks. Physica A: Statistical Mechanics and Its Applications, 522, 98–111. https://doi.org/10.1016/j.physa.2019.01.139
Gupta, P., & Sharma, K. (2017). Review paper on various clustering schemes. 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), 37(August), 44–48. https://doi.org/10.1109/ICSTM.2017.8089125
Haghbayan, S. A., Geroliminis, N., & Akbarzadeh, M. (2021). Community detection in large scale congested urban road networks. PLOS ONE, 16(11), e0260201. https://doi.org/10.1371/journal.pone.0260201
Hairol Anuar, S. H., Abal Abas, Z., Md Yunos, N., Mukhtar, M. F., Setiadi, T., & Shibghatullah, A. S. (2024). Identifying Communities with Modularity Metric Using Louvain and Leiden Algorithms. Pertanika Journal of Science and Technology, 32(3), 1285–1300. https://doi.org/10.47836/pjst.32.3.16
Hairol Anuar, S. H., Abas, Z. A., Yunos, N. M., Mohd Zaki, N. H., Hashim, N. A., Mokhtar, M. F., Asmai, S. A., Abidin, Z. Z., & Nizam, A. F. (2021). Comparison between Louvain and Leiden Algorithm for Network Structure: A Review. Journal of Physics: Conference Series, 2129(1), 012028. https://doi.org/10.1088/1742-6596/2129/1/012028
Han, X., Huangpeng, Q., Duan, X., Gao, Q., & Yin, Y. (2022). Intentional controlled islanding based on dynamic community detection for power grid. IET Generation, Transmission & Distribution, 16(21), 4258–4272. https://doi.org/10.1049/gtd2.12591
Han, Z., Mo, R., Yang, H., & Hao, L. (2018). Module partition for mechanical CAD assembly model based on multi-source correlation information and community detection. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 12(1), JAMDSM0023–JAMDSM0023. https://doi.org/10.1299/jamdsm.2018jamdsm0023
Heinz, B., & Henkel, J. (2012). Balancing wind energy and participating in electricity markets with a fuel cell population. Energy, 48(1), 188–195. https://doi.org/10.1016/j.energy.2012.07.002
Huang, L., Yang, Y., Gao, H., Zhao, X., & Du, Z. (2018). Comparing Community Detection Algorithms in Transport Networks via Points of Interest. IEEE Access, 6, 29729–29738. https://doi.org/10.1109/ACCESS.2018.2841321
Irsyad, A., & Rakhmawati, N. A. (2019). Community detection in twitter based on tweets similarities in indonesian using cosine similarity and louvain algorithms. Register: Jurnal Ilmiah Teknologi Sistem Informasi, 6(1), 22. https://doi.org/10.26594/register.v6i1.1595
Javed, M. A., Younis, M. S., Latif, S., Qadir, J., & Baig, A. (2018). Community detection in networks: A multidisciplinary review. Journal of Network and Computer Applications, 108(January), 87–111. https://doi.org/10.1016/j.jnca.2018.02.011
Javed, S., Mahmood, A., Werghi, N., Benes, K., & Rajpoot, N. (2020). Multiplex Cellular Communities in Multi-Gigapixel Colorectal Cancer Histology Images for Tissue Phenotyping. IEEE Transactions on Image Processing, 29, 9204–9219. https://doi.org/10.1109/TIP.2020.3023795
Jiang, C., Wan, J., & Abbas, H. (2021). An Edge Computing Node Deployment Method Based on Improved k -Means Clustering Algorithm for Smart Manufacturing. IEEE Systems Journal, 15(2), 2230–2240. https://doi.org/10.1109/JSYST.2020.2986649
Jiang, L., Shi, L., Liu, L., Yao, J., Yuan, B., & Zheng, Y. (2019). An Efficient Evolutionary User Interest Community Discovery Model in Dynamic Social Networks for Internet of People. IEEE Internet of Things Journal, 6(6), 9226–9236. https://doi.org/10.1109/JIOT.2019.2893625
Jiang, Z., Chen, X., Dong, B., Zhang, J., Gong, J., Yan, H., Zhang, Z., Ma, J., & Yu, P. S. (2020). Trajectory-Based Community Detection. IEEE Transactions on Circuits and Systems II: Express Briefs, 67(6), 1139–1143. https://doi.org/10.1109/TCSII.2019.2933337
Jin, C., Jin, R., Chen, K., & Dou, Y. (2018). A Community Detection Approach to Cleaning Extremely Large Face Database. Computational Intelligence and Neuroscience, 2018, 1–10. https://doi.org/10.1155/2018/4512473
Jin, D., Li, R., & Xu, J. (2020). Multiscale Community Detection in Functional Brain Networks Constructed Using Dynamic Time Warping. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(1), 52–61. https://doi.org/10.1109/TNSRE.2019.2948055
Kabir, K., Hassan, L., Rajabi, Z., Akhter, N., & Shehu, A. (2019). Graph-Based Community Detection for Decoy Selection in Template-Free Protein Structure Prediction. Molecules, 24(5), 854. https://doi.org/10.3390/molecules24050854
Kadkhoda Mohammadmosaferi, K., & Naderi, H. (2020). Evolution of communities in dynamic social networks: An efficient map-based approach. Expert Systems with Applications, 147, 113221. https://doi.org/10.1016/j.eswa.2020.113221
Kannan, S. N., Mannathazhathu, S. E., & Raghavan, R. (2022). A novel compression based community detection approach using hybrid honey badger African vulture optimization for online social networks. Concurrency and Computation: Practice and Experience, 34(23). https://doi.org/10.1002/cpe.7205
Karaaslanli, A., Ortiz-Bouza, M., Munia, T. T. K., & Aviyente, S. (2023). Community detection in multi-frequency EEG networks. Scientific Reports, 13(1), 8114. https://doi.org/10.1038/s41598-023-35232-2
Karatas, A., & Sahin, S. (2018). Application Areas of Community Detection: A Review. 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), ii, 65–70. https://doi.org/10.1109/IBIGDELFT.2018.8625349
Karyotis, V., Tsitseklis, K., Sotiropoulos, K., & Papavassiliou, S. (2018). Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications. Sensors, 18(4), 1205. https://doi.org/10.3390/s18041205
Kramer, J., Boone, L., Clifford, T., Bruce, J., & Matta, J. (2020). Analysis of Medical Data Using Community Detection on Inferred Networks. IEEE Journal of Biomedical and Health Informatics, 24(11), 3136–3143. https://doi.org/10.1109/JBHI.2020.3003827
Lancichinetti, A., & Fortunato, S. (2009). Community detection algorithms: A comparative analysis. Physical Review E, 80(5), 056117. https://doi.org/10.1103/PhysRevE.80.056117
Li, J., Li, X., Gao, Y., Yuan, J., & Fang, B. (2018). Dynamic Trustworthiness Overlapping Community Discovery in Mobile Internet of Things. IEEE Access, 6, 74579–74597. https://doi.org/10.1109/ACCESS.2018.2884002
Li, Q., Liu, Y., Zhu, J., Xiao, Y., Wu, H., Liu, X., Chen, Z., & Zhai, S. (2019). ANDERATION: A New Anti-Community Detection Algorithm and its Application to Explore Incompatibility of Traditional Chinese Medicine. IEEE Access, 7, 113975–113987. https://doi.org/10.1109/ACCESS.2019.2934227
Li, S., Zhao, C., Li, Q., Huang, J., Zhao, D., & Zhu, P. (2023). BotFinder: a novel framework for social bots detection in online social networks based on graph embedding and community detection. World Wide Web, 26(4), 1793–1809. https://doi.org/10.1007/s11280-022-01114-2
Li, Z., Li, Y., Lu, W., & Huang, J. (2020). Crowdsourcing Logistics Pricing Optimization Model Based on DBSCAN Clustering Algorithm. IEEE Access, 8, 1–1. https://doi.org/10.1109/ACCESS.2020.2995063
Lijcklama à Nijeholt, D. (2020). Control for Cooperative Autonomous Driving Inspired by Bird Flocking Behavior.
Lin, D., & Wang, Q. (2019). An Energy-Efficient Clustering Algorithm Combined Game Theory and Dual-Cluster-Head Mechanism for WSNs. IEEE Access, 7, 49894–49905. https://doi.org/10.1109/ACCESS.2019.2911190
Liu, X., Ding, N., Fiumara, G., De Meo, P., & Ficara, A. (2022). Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal Networks. Applied Sciences, 12(8), 3795. https://doi.org/10.3390/app12083795
Liu, X., & Qiu, L. (2019). Bird Flocking Inspired Control Strategy for Multi-UAV Collective Motion. 1, 1–7. http://arxiv.org/abs/1912.00168
Lu, Z., & Dong, Z. (2023). A Gravitation-Based Hierarchical Community Detection Algorithm for Structuring Supply Chain Network. International Journal of Computational Intelligence Systems, 16(1), 110. https://doi.org/10.1007/s44196-023-00290-x
Malekshahi Rad, M., Rahmani, A. M., Sahafi, A., & Qader, N. N. (2023). Community detection and service discovery on Social Internet of Things. International Journal of Communication Systems, 36(11). https://doi.org/10.1002/dac.5501
Mark Needham, A. E. H. (2021). Graph algorithms Practical examples in Apache Spark and Neo4j. In O’reilly. Neo4j.
Meena, P., Pawar, M., & Pandey, A. (2021). A Survey on Community Detection Algorithm and Its Applications. Turkish Journal of Computer and Mathematics Education, 12(6), 4807–4815.
Milano, M., & Cannataro, M. (2020). Statistical and Network-Based Analysis of Italian COVID-19 Data: Communities Detection and Temporal Evolution. International Journal of Environmental Research and Public Health, 17(12), 4182. https://doi.org/10.3390/ijerph17124182
Mnea, A., & Zairul, M. (2023). Evaluating the Impact of Housing Interior Design on Elderly Independence and Activity: A Thematic Review. Buildings, 13(4), 1099. https://doi.org/10.3390/buildings13041099
Mohd Zairul. (2021). A thematic Review on Industrialised Building System (IBS) Publications from 2015-2019: Analysis of Patterns and Trends for Future Studies of IBS in Malaysia. Pertanika Journal of Social Sciences and Humanities, 29(1), 635–652. https://doi.org/10.47836/pjssh.29.1.35
Montes-Orozco, E., Mora-Gutiérrez, R. A., De-Los-Cobos-Silva, S. G., Bernal-Jaquez, R., Rincón-García, E. A., Gutiérrez-Andrade, M. A., & Lara-Velázquez, P. (2023). Communities Detection in Multiplex Networks Using Optimization: Study Case—Employment in Mexico during the COVID-19 Pandemic. Complexity, 2023, 1–14. https://doi.org/10.1155/2023/9011738
Moradi, S., Mohasefi, J. B., & Mahdipour, E. (2021). MSN?CDF: New community detection framework to improve routing in mobile social networks. International Journal of Communication Systems, 34(18). https://doi.org/10.1002/dac.4989
Moscato, V., & Sperlì, G. (2022). Community detection over feature-rich information networks: An eHealth case study. Information Systems, 109, 102092. https://doi.org/10.1016/j.is.2022.102092
Mueller, J. M., Pritschet, L., Santander, T., Taylor, C. M., Grafton, S. T., Jacobs, E. G., & Carlson, J. M. (2021). Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle. Network Neuroscience, 5(1), 125–144. https://doi.org/10.1162/netn_a_00169
Mukhtar, M. F., Abal Abas, Z., Baharuddin, A. S., Norizan, M. N., Fakhruddin, W. F. W. W., Minato, W., Rasib, A. H. A., Abidin, Z. Z., Rahman, A. F. N. A., & Anuar, S. H. H. (2023). Integrating local and global information to identify influential nodes in complex networks. Scientific Reports, 13(1), 11411. https://doi.org/10.1038/s41598-023-37570-7
Mukhtar, M. F., Abas, Z. A., Rasib, A. H. A., Anuar, S. H. H., Zaki, N. H. M., Abidin, Z. Z., Asmai, S. A., & Rahman, A. F. N. A. (2023). Graph analytics’ centrality measurement in supply chain. Journal of Physics: Conference Series, 060001. https://doi.org/10.1063/5.0148751
Mukhtar, M. F., Abas, Z. A., Rasib, A. H. A., Anuar, S. H. H., Zaki, N. H. M., Rahman, A. F. N. A., Abidin, Z. Z., & Shibghatullah, A. S. (2023). Hybrid Global Structure Model for Unraveling Influential Nodes in Complex Networks. International Journal of Advanced Computer Science and Applications, 14(6), 724–730. https://doi.org/10.14569/IJACSA.2023.0140677
Naik, D., Ramesh, D., Gandomi, A. H., & Babu Gorojanam, N. (2022). Parallel and distributed paradigms for community detection in social networks: A methodological review. Expert Systems with Applications, 187(October 2020), 115956. https://doi.org/10.1016/j.eswa.2021.115956
Nallusamy, K., & Easwarakumar, K. S. (2023). Classifying schizophrenic and controls from fMRI data using graph theoretic framework and community detection. Network Modeling Analysis in Health Informatics and Bioinformatics, 12(1), 19. https://doi.org/10.1007/s13721-023-00415-4
Nicolini, C., Bordier, C., & Bifone, A. (2017a). Community detection in weighted brain connectivity networks beyond the resolution limit. NeuroImage, 146(October 2016), 28–39. https://doi.org/10.1016/j.neuroimage.2016.11.026
Nicolini, C., Bordier, C., & Bifone, A. (2017b). Community detection in weighted brain connectivity networks beyond the resolution limit. NeuroImage, 146, 28–39. https://doi.org/https://doi.org/10.1016/j.neuroimage.2016.11.026
Ou, Q., Jin, Y. Di, Zhou, T., Wang, B. H., & Yin, B. Q. (2007). Power-law strength-degree correlation from resource-allocation dynamics on weighted networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 75(2), 1–6. https://doi.org/10.1103/PhysRevE.75.021102
Park, J.-H., & Kwon, H.-Y. (2022). Cyberattack detection model using community detection and text analysis on social media. ICT Express, 8(4), 499–506. https://doi.org/10.1016/j.icte.2021.12.003
Park, J. H., & Kwon, H. Y. (2022). Cyberattack detection model using community detection and text analysis on social media. ICT Express, 8(4), 499–506. https://doi.org/10.1016/j.icte.2021.12.003
Peeples, M. A., & J. Bischoff, R. (2023). Archaeological networks, community detection, and critical scales of interaction in the U.S. Southwest/Mexican Northwest. Journal of Anthropological Archaeology, 70(March), 101511. https://doi.org/10.1016/j.jaa.2023.101511
Pereira, L. R., Lopes, R. J., & Louçã, J. (2021). Community identity in a temporal network: A taxonomy proposal. Ecological Complexity, 45(December 2020), 100904. https://doi.org/10.1016/j.ecocom.2020.100904
Qi, Q., & Cao, J. (2023). METHODS: A meta-path-based method for heterogeneous community detection in the open source software ecosystem. Information and Software Technology, 162(November 2022), 107271. https://doi.org/10.1016/j.infsof.2023.107271
Rahiminejad, S., Maurya, M. R., & Subramaniam, S. (2019). Topological and functional comparison of community detection algorithms in biological networks. BMC Bioinformatics, 20(1). https://doi.org/10.1186/s12859-019-2746-0
Reddy, V., Kolli, N., & Balakrishnan, N. (2021). Malware detection and classification using community detection and social network analysis. Journal of Computer Virology and Hacking Techniques, 17(4), 333–346. https://doi.org/10.1007/s11416-021-00387-x
Rostami, M., Farrahi, V., Ahmadian, S., Mohammad Jafar Jalali, S., & Oussalah, M. (2023). A novel healthy and time-aware food recommender system using attributed community detection. Expert Systems with Applications, 221(November 2022), 119719. https://doi.org/10.1016/j.eswa.2023.119719
Saggese, F., Moretti, M., & Abrardo, A. (2020). A Quasi-Optimal Clustering Algorithm for MIMO-NOMA Downlink Systems. IEEE Wireless Communications Letters, 9(2), 152–156. https://doi.org/10.1109/LWC.2019.2946548
Samsudin, N. S., Mohammad, M. Z., Khalil, N., Nadzri, N. D., & Izam Che Ibrahim, C. K. (2022). A thematic review on Prevention through design (PtD) concept application in the construction industry of developing countries. Safety Science, 148(July 2021), 105640. https://doi.org/10.1016/j.ssci.2021.105640
Sangaiah, A. K., Rezaei, S., Javadpour, A., & Zhang, W. (2023). Explainable AI in big data intelligence of community detection for digitalization e-healthcare services. Applied Soft Computing, 136, 110119. https://doi.org/10.1016/j.asoc.2023.110119
Shahruddin, S., Zairul, M., & Haron, A. T. (2020). Redefining the territory and competency of architectural practitioners within a BIM-based environment: a systematic review. Architectural Engineering and Design Management, 0(0), 1–35. https://doi.org/10.1080/17452007.2020.1768506
Shrifan, N. H. M. M., Jawad, G. N., Isa, N. A. M., & Akbar, M. F. (2021). Microwave Nondestructive Testing for Defect Detection in Composites Based on K-Means Clustering Algorithm. IEEE Access, 9, 4820–4828. https://doi.org/10.1109/ACCESS.2020.3048147
Singhal, A., Cao, S., Churas, C., Pratt, D., Fortunato, S., Zheng, F., & Ideker, T. (2020). Multiscale community detection in Cytoscape. PLOS Computational Biology, 16(10), e1008239. https://doi.org/10.1371/journal.pcbi.1008239
Su, X., Xue, S., Liu, F., Wu, J., Yang, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Jin, D., Sheng, Q. Z., & Yu, P. S. (2024). A Comprehensive Survey on Community Detection With Deep Learning. IEEE Transactions on Neural Networks and Learning Systems, Xx, 1–21. https://doi.org/10.1109/TNNLS.2021.3137396
Subbian, K., Aggarwal, C., & Srivastava, J. (2016). Mining influencers using information flows in social streams. ACM Transactions on Knowledge Discovery from Data, 10(3). https://doi.org/10.1145/2815625
Talib, M. S., Hassan, A., Alamery, T., Abas, Z. A., Mohammed, A. A.-J. J., Ibrahim, A. J., & Abdullah, N. I. (2020). A center-based stable evolving clustering algorithm with grid partitioning and extended mobility features for VANETs. IEEE Access, 8, 169908–169921. https://doi.org/10.1109/ACCESS.2020.3020510
Torene, S., Follmann, A., Teague, T., Chang, P., & Howald, B. (2022). Automated Hashtag Hierarchy Generation Using Community Detection and the Shannon Diversity Index, with Applications to Twitter and Parler. International Journal of Semantic Computing, 16(04), 473–496. https://doi.org/10.1142/S1793351X22500052
Wang, C., & Wang, F. (2022). GIS-automated delineation of hospital service areas in Florida: from Dartmouth method to network community detection methods. Annals of GIS, 28(2), 93–109. https://doi.org/10.1080/19475683.2022.2026470
Wang, J., & Paschalidis, I. C. (2017). Botnet Detection Based on Anomaly and Community Detection. IEEE Transactions on Control of Network Systems, 4(2), 392–404. https://doi.org/10.1109/TCNS.2016.2532804
Wang, J., Zhou, C., Rong, J., Liu, S., & Wang, Y. (2022). Community-detection-based spatial range identification for assessing bilateral jobs-housing balance: The case of Beijing. Sustainable Cities and Society, 87(September), 104179. https://doi.org/10.1016/j.scs.2022.104179
Wang, T., Guo, J., Shan, Y., Zhang, Y., Peng, B., & Wu, Z. (2023). A knowledge graph–GCN–community detection integrated model for large-scale stock price prediction. Applied Soft Computing, 145, 110595. https://doi.org/10.1016/j.asoc.2023.110595
White, S., & Smyth, P. (2005). A spectral clustering approach to finding communities in graphs. Proceedings of the 2005 SIAM International Conference on Data Mining, SDM 2005, 274–285. https://doi.org/10.1137/1.9781611972757.25
Wu, J., Wang, Y., Shafiee, S., & Zhang, D. (2021). Discovery of associated consumer demands: Construction of a co-demanded product network with community detection. Expert Systems with Applications, 178, 115038. https://doi.org/10.1016/j.eswa.2021.115038
Wu, S. X., Wu, Z., Chen, S., Li, G., & Zhang, S. (2021). Community Detection in Blockchain Social Networks. Journal of Communications and Information Networks, 6(1), 59–71. https://doi.org/10.23919/JCIN.2021.9387705
Wu, W., Zhang, H., Zhang, S., & Witlox, F. (2019). Community Detection in Airline Networks: An Empirical Analysis of American vs. Southwest Airlines. Journal of Advanced Transportation, 2019, 1–11. https://doi.org/10.1155/2019/3032015
Xie, L., Cui, J., Qin, Y., & Qiu, L. (2022). Analysis of deformation characteristics of reverse slope under the influence of reservoir water based on community detection. Environmental Earth Sciences, 81(4), 110. https://doi.org/10.1007/s12665-022-10252-9
Xue, C., Wu, S., Zhang, Q., & Shao, F. (2019a). An incremental group-specific framework based on community detection for cold start recommendation. IEEE Access, 7, 112363–112374. https://doi.org/10.1109/ACCESS.2019.2935090
Xue, C., Wu, S., Zhang, Q., & Shao, F. (2019b). An Incremental Group-Specific Framework Based on Community Detection for Cold Start Recommendation. IEEE Access, 7, 112363–112374. https://doi.org/10.1109/ACCESS.2019.2935090
Xueshuo, X., Jiming, W., Junyi, Y., Yaozheng, F., Ye, L., Tao, L., & Guiling, W. (2021). AWAP: Adaptive weighted attribute propagation enhanced community detection model for bitcoin de-anonymization. Applied Soft Computing, 109, 107507. https://doi.org/10.1016/j.asoc.2021.107507
Yan, Y., & Yang, Y. (2023). Community detection for New York stock market by SCORE-CCD. Computational Statistics, 38(3), 1255–1282. https://doi.org/10.1007/s00180-022-01245-0
Yang, B., Huang, X., Cheng, W., Huang, T., & Li, X. (2022). Discrete bacterial foraging optimization for community detection in networks. Future Generation Computer Systems, 128, 192–204. https://doi.org/10.1016/j.future.2021.10.015
Yang, H., & Le, M. (2021). High-Order Community Detection in the Air Transport Industry: A Comparative Analysis among 10 Major International Airlines. Applied Sciences, 11(20), 9378. https://doi.org/10.3390/app11209378
Yang, Y., Sun, Y., Wang, Q., Liu, F., & Zhu, L. (2022). Fast Power Grid Partition for Voltage Control With Balanced-Depth-Based Community Detection Algorithm. IEEE Transactions on Power Systems, 37(2), 1612–1622. https://doi.org/10.1109/TPWRS.2021.3107847
Yaqoob, A. F., & Al-Sarray, B. (2020). Community detection under stochastic block model likelihood optimization via tabu search –Fuzzy C-mean method for social network data. Iraqi Journal of Science, 61(10), 2695–2704. https://doi.org/10.24996/ijs.2020.61.10.27
Zairul, M. (2022). Opening the pandora’s box of issues in the industrialised building system (ibs) in malaysia: A thematic review. Journal of Applied Science and Engineering (Taiwan), 25(2), 297–310. https://doi.org/10.6180/jase.202204_25(2).0006
Zairul, M., Azli, M., & Azlan, A. (2023). Defying tradition or maintaining the status quo? Moving towards a new hybrid architecture studio education to support blended learning post-COVID-19. International Jouranl of Architectural Research: Archnet-IJAR. https://doi.org/10.1108/ARCH-11-2022-0251
Zairul, M., Mohd Zairul, & Zairul, M. (2020). A thematic review on student-centred learning in the studio education. Journal of Critical Reviews, 7(2), 504–511. https://doi.org/10.31838/jcr.07.02.95
Zairul, M., & Zaremohzzabieh, Z. (2023). Thematic Trends in Industry 4.0 Revolution Potential towards Sustainability in the Construction Industry. Sustainability, 15(9), 7720. https://doi.org/10.3390/su15097720
Zakaria, A., & Isa, N. M. (2022). THEMATIC REVIEW ON ISLAMIC DESIGN QUALITY IN. 7(27), 417–429. https://doi.org/10.35631/JTHEM.727033.This
Zhang, C., Zheng, B., & Tsung, F. (2023). Multi-view metro station clustering based on passenger flows: a functional data-edged network community detection approach. Data Mining and Knowledge Discovery, 37(3), 1154–1208. https://doi.org/10.1007/s10618-023-00916-w
Zhang, Q., Tan, V. Y. F. F., & Suh, C. (2021). Community Detection and Matrix Completion with Social and Item Similarity Graphs. IEEE Transactions on Signal Processing, 69, 917–931. https://doi.org/10.1109/TSP.2021.3052033
Zhang, S., Zhang, Y., Zhou, M., & Peng, L. (2022). Community detection based on similarities of communication behavior in IP networks. Journal of Ambient Intelligence and Humanized Computing, 13(3), 1451–1461. https://doi.org/10.1007/s12652-020-02681-w
Zhang, W., Zhang, R., Shang, R., Li, J., & Jiao, L. (2019). Application of natural computation inspired method in community detection. Physica A: Statistical Mechanics and Its Applications, 515, 130–150. https://doi.org/10.1016/j.physa.2018.09.186
Zhang, Y., Liu, Y., Zhu, J., Zhai, S., Jin, R., & Wen, C. (2020). A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study. Computational and Mathematical Methods in Medicine, 2020, 1–23. https://doi.org/10.1155/2020/1862168
Zhang, Y., Pan, R., Wang, H., & Su, H. (2023). Community detection in attributed collaboration network for statisticians. Stat, 12(1). https://doi.org/10.1002/sta4.507
Zhang, Y., Wu, B., Ning, N., Song, C., & Lv, J. (2019). Dynamic Topical Community Detection in Social Network: A Generative Model Approach. IEEE Access, 7, 74528–74541. https://doi.org/10.1109/ACCESS.2019.2921824
Zhao, Y., Chen, B. Y., Gao, F., & Zhu, X. (2023). Dynamic community detection considering daily rhythms of human mobility. Travel Behaviour and Society, 31(December 2022), 209–222. https://doi.org/10.1016/j.tbs.2022.12.009
Zhou, Z., Chen, B., Chen, S., Lin, M., Chen, Y., Jin, S., Chen, W., & Zhang, Y. (2020). Applications of Network Pharmacology in Traditional Chinese Medicine Research. Evidence-Based Complementary and Alternative Medicine, 2020, 1–7. https://doi.org/10.1155/2020/1646905
Zhu, Y., Liu, J., & Cong, F. (2023). Dynamic Community Detection for Brain Functional Networks During Music Listening With Block Component Analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31(Xx), 2438–2447. https://doi.org/10.1109/TNSRE.2023.3277509
Zhuo, L., Chen, Y., Song, B., Liu, Y., & Su, Y. (2022). A model for predicting ncRNA–protein interactions based on graph neural networks and community detection. Methods, 207(September 2022), 74–80. https://doi.org/10.1016/j.ymeth.2022.09.001
Zu, J., Hu, G., Yan, J., & Tang, S. (2021). A community detection based approach for Service Function Chain online placement in data center network. Computer Communications, 169, 168–178. https://doi.org/10.1016/j.comcom.2021.01.014.

Anuar, S. H. H., Abas, Z. A., Mukhtar, M. F., & Miswan, N. H. (2024). Community Detection in Practice: A Review of Real-World Applications Across Six Themes. International Journal of Academic Research in Business and Social Sciences, 14(10), 953–996.

Copyright: © 2024 The Author(s)
Published by Knowledge Words Publications (www.kwpublications.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode