{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:47:07Z","timestamp":1767340027717,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T00:00:00Z","timestamp":1586390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"undefined  &lt;span style=&quot;color:gray;font-size:10px;&quot;&gt;undefined&lt;\/span&gt;","award":["5038701"],"award-info":[{"award-number":["5038701"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Presently, due to the extended availability of gigantic information networks and the beneficial application of graph analysis in various scientific fields, the necessity for efficient and highly scalable community detection algorithms has never been more essential. Despite the significant amount of published research, the existing methods\u2014such as the Girvan\u2013Newman, random-walk edge betweenness, vertex centrality, InfoMap, spectral clustering, etc.\u2014have virtually been proven incapable of handling real-life social graphs due to the intrinsic computational restrictions that lead to mediocre performance and poor scalability. The purpose of this article is to introduce a novel, distributed community detection methodology which in accordance with the community prediction concept, leverages the reduced complexity and the decreased variance of the bagging ensemble methods, to unveil the subjacent community hierarchy. The proposed approach has been thoroughly tested, meticulously compared against different classic community detection algorithms, and practically proven exceptionally scalable, eminently efficient, and promisingly accurate in unfolding the underlying community structure.<\/jats:p>","DOI":"10.3390\/info11040199","type":"journal-article","created":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T14:42:03Z","timestamp":1586443323000},"page":"199","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Distributed Bagging Ensemble Methodology for Community Prediction in Social Networks"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2571-7449","authenticated-orcid":false,"given":"Christos","family":"Makris","sequence":"first","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9765-5542","authenticated-orcid":false,"given":"Georgios","family":"Pispirigos","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis Orestis","family":"Rizos","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,9]]},"reference":[{"key":"ref_1","unstructured":"(2020, February 12). Internet Fact. Available online: https:\/\/hostingfacts.com\/internet-facts-stats\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.cosrev.2007.05.001","article-title":"Graph Clustering","volume":"1","author":"Schaeffer","year":"2007","journal-title":"Comput. Sci. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lancichinetti, A., Kivel\u00e4, M., Saram\u00e4ki, J., and Fortunato, S. (2010). Characterizing the community structure of complex networks. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0011976"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Makris, C., Pettas, D., and Pispirigos, G. (2019). Distributed Community Prediction for Social Graphs Based on Louvain Algorithm. AIAI, 500\u2013511.","DOI":"10.1007\/978-3-030-19823-7_42"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fortunato, S. (2009). Community detection in graphs. CoRR. arXiv.","DOI":"10.1007\/978-0-387-30440-3_76"},{"key":"ref_6","first-page":"026113","article-title":"Finding and Evaluating Community Structure in Networks","volume":"69","author":"Newman","year":"2004","journal-title":"Phys. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7821","DOI":"10.1073\/pnas.122653799","article-title":"Community structure in social and biological networks","volume":"99","author":"Girvan","year":"2002","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1038\/s41598-017-02751-8","article-title":"Node Attribute-enhanced Community Detection in Complex Networks","volume":"7","author":"Jia","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.procs.2016.06.082","article-title":"An Analysis of Overlapping Community Detection Algorithms in Social Networks","volume":"89","author":"Devi","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of community hierarchies in large networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"J. Stat. Mech. Theory Exp."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e1602548","DOI":"10.1126\/sciadv.1602548","article-title":"The ground truth about metadata and community detection in networks","volume":"3","author":"Peel","year":"2017","journal-title":"Sci. Adv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Takaffoli, M., Rabbany, R., and Za\u00efane, O.R. (2014, January 17\u201320). Community Evolution Prediction in Dynamic Social Networks. Proceedings of the 2014 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), Beijing, China.","DOI":"10.1109\/ASONAM.2014.6921553"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Appel, A.P., Cunha, R.L., Aggarwal, C.C., and Terakado, M.M. (2018, January 10\u201314). Temporally Evolving Community Detection and Prediction in Content-Centric Networks. Proceedings of the European Conference, ECML PKDD 2018, Dublin, Ireland.","DOI":"10.1007\/978-3-030-10928-8_1"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cherifi, H., Gon\u00e7alves, B., Menezes, R., and Sinatra, R. (2016). Improving Network Community Structure with Link Prediction Ranking. Complex Networks VII, Springer International Publishing.","DOI":"10.1007\/978-3-319-30569-1"},{"key":"ref_15","unstructured":"Cheng, H.-M., and Zhang, Z. (2016). Community Detection Based on Link Prediction Methods. CoRR. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zamani, M., Schwartz, H.A., Lynn, V.E., Giorgi, S., and Balasubramanian, N. (2018). Residualized Factor Adaptation for Community Social Media Prediction Tasks. arXiv.","DOI":"10.18653\/v1\/D18-1392"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shao, J., Zhang, Z., Yu, Z., Wang, J., Zhao, Y., and Yang, Q. (2019, January 10\u201316). Community Detection and Link Prediction via Cluster-driven Low-rank Matrix Completion. Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China.","DOI":"10.24963\/ijcai.2019\/469"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gao, F., Musial, K., and Gabrys, B. (August, January 31). A Community Bridge Boosting Social Network Link Prediction Model. Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, Sydney, Australia.","DOI":"10.1145\/3110025.3110143"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"43233","DOI":"10.1109\/ACCESS.2019.2908208","article-title":"Effective Link Prediction Based on Community Relationship Strength","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","unstructured":"Sethu, H., and Chu, X. (2012). A new algorithm for extracting a small representative subgraph from a very large graph. arXiv."},{"key":"ref_21","unstructured":"Cukierski, W., Hamner, B., and Yang, B. (August, January 31). Graph-based features for supervised link prediction. Proceedings of the International Joint Conference on Neural Networks, San Jose, CA, USA."},{"key":"ref_22","unstructured":"(2020, February 12). Sotera Distributed Graph Analytics (DGA): Sotera Defence Solution. Available online: https:\/\/github.com\/Sotera\/spark-distributed-louvain-modularity.git."},{"key":"ref_23","unstructured":"Kranda, D. (2012). The Square of Adjacency Matrices. arXiv."},{"key":"ref_24","first-page":"1235","article-title":"MLlib: Machine Learning in Apache Spark","volume":"17","author":"Meng","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_25","unstructured":"(2020, January 12). NetworkX Clauset\u2013Newman\u2013Moore Implementation. Available online: https:\/\/networkx.github.io\/documentation\/latest\/reference\/algorithms\/generated\/networkx.algorithms.community.modularity_max.greedy_modularity_communities.html."},{"key":"ref_26","unstructured":"(2020, February 12). Zachary Karate Club Network Dataset. KONECT, April 2017. Available online: http:\/\/konect.uni-koblenz.de\/networks\/ucidata-zachary."},{"key":"ref_27","unstructured":"(2020, February 12). Dolphins Network Dataset\u2014KONECT, April 2017. Available online: http:\/\/konect.uni-koblenz.de\/networks\/dolphins."},{"key":"ref_28","unstructured":"(2020, February 12). Hamster Friendships Network Dataset. KONECT, April 2017. Available online: http:\/\/konect.uni-koblenz.de\/networks\/petster-friendships-hamster."},{"key":"ref_29","unstructured":"Kumar, S., Hooi, B., Makhija, D., Kumar, M., Subrahmanian, V.S., and Faloutsos, C. (2020, February 12). REV2: Fraudulent User Prediction in Rating Platforms. 11th ACM International Conference on Web Searchand Data Mining (WSDM). Available online: https:\/\/snap.stanford.edu\/data\/soc-sign-bitcoin-alpha.html."},{"key":"ref_30","unstructured":"Yin, H., Benson, A.R., Leskovec, J., and Gleich, D.F. (2020, February 12). Local Higher-order Graph Clustering. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Available online: https:\/\/snap.stanford.edu\/data\/email-Eu-core.html."},{"key":"ref_31","unstructured":"McAuley, J., and Leskovec, J. (2020, February 12). Learning to Discover Social Circles in Ego Networks. NIPS. Available online: https:\/\/snap.stanford.edu\/data\/ego-Facebook.html."},{"key":"ref_32","unstructured":"Klimmt, B., and Yang, Y. (2020, February 12). Introducing the Enron corpus. CEAS Conference. Available online: https:\/\/snap.stanford.edu\/data\/email-Enron.html."},{"key":"ref_33","unstructured":"(2020, February 12). Douban Network Dataset. KONECT, April 2017. Available online: http:\/\/konect.uni-koblenz.de\/networks\/douban."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Richardson, M., Agrawal, R., and Domingos, P. (2020, February 12). Trust Management for the Semantic Web. ISWC, 2003. Available online: https:\/\/snap.stanford.edu\/data\/soc-Epinions1.html.","DOI":"10.1007\/978-3-540-39718-2_23"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/4\/199\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:16:47Z","timestamp":1760174207000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/4\/199"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,9]]},"references-count":34,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["info11040199"],"URL":"https:\/\/doi.org\/10.3390\/info11040199","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2020,4,9]]}}}