{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:13:36Z","timestamp":1760242416858,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2017,6,29]],"date-time":"2017-06-29T00:00:00Z","timestamp":1498694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In social networking analysis, there exists a fundamental problem called maximal cliques enumeration(MCE), which has been extensively investigated in many fields, including social networks, biological science, etc. As a matter of fact, the formation principle of maximal cliques that can help us to speed up the detection of maximal cliques from social networks is often ignored by most existing research works. Aiming to exploit the formation of maximal cliques in social networks, this paper pioneers a creative research issue on the detection of bases of maximal cliques in social networks. We propose a formal concept analysis-based approach for detecting the bases of maximal cliques and detection theorem. It is believed that our work can provide a new research solution and direction for future topological structure analysis in various complex networking systems.<\/jats:p>","DOI":"10.3390\/sym9070100","type":"journal-article","created":{"date-parts":[[2017,6,29]],"date-time":"2017-06-29T10:40:04Z","timestamp":1498732804000},"page":"100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Exploiting the Formation of Maximal Cliques in Social Networks"],"prefix":"10.3390","volume":"9","author":[{"given":"Fei","family":"Hao","sequence":"first","affiliation":[{"name":"Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi\u2019an 710062, China"},{"name":"School of Computer Science, Shaanxi Normal University, Xi\u2019an 710119, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Doo-Soon","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Pei","sequence":"additional","affiliation":[{"name":"Center for Radio Administration &amp; Technology Development, Xihua University, Chengdu 610039, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,29]]},"reference":[{"key":"ref_1","unstructured":"Conte, A., De Virgilio, R., Maccioni, A., Patrignani, M., and Torlone, R. (2016, January 15\u201318). Finding All Maximal Cliques in Very Large Social Networks. Proceedings of the Extending Database Technology (EDBT), Bordeaux, France."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/TSC.2015.2479225","article-title":"Distributed Maximal Clique Computation and Management","volume":"9","author":"Xu","year":"2016","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Modani, N., and Dey, K. (2008, January 26\u201330). Large Maximal Cliques Enumeration in Large Sparse Graphs. Proceedings of the 17th ACM conference on Information and knowledge management, Napa Valley, CA, USA.","DOI":"10.1145\/1458082.1458288"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Eppstein, D., Loffler, M., and Strash, D. (2010, January 15\u201317). Listing All Maximal Cliques in Sparse Graphs in Near-Optimal Time. Proceedings of the 21st International Symposium on Algorithms and Computation, Jeju Island, Korea.","DOI":"10.1007\/978-3-642-17517-6_36"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cheng, J., Zhu, L., Ke, Y., and Chu, S. (2012, January 12\u201316). Fast Algorithms for Maximal Clique Enumeration with Limited Memory. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China.","DOI":"10.1145\/2339530.2339724"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Goodrich, M.T., and Pszona, P. (2011, January 5\u20139). External-Memory Network Analysis Algorithms for Naturally Sparse Graphs. Proceedings of the 19th Europe Conference on Algorithms, Saarbr\u00fccken, Germany.","DOI":"10.1007\/978-3-642-23719-5_56"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Du, N., Wu, B., Xu, L., Wang, B., and Xin, P. (2009). Parallel Algorithm for Enumerating Maximal Cliques in Complex Network. Mining Complex Data, Springer.","DOI":"10.1007\/978-3-540-88067-7_12"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.jpdc.2009.01.003","article-title":"A scalable, parallel algorithm for maximal clique enumeration","volume":"69","author":"Schmidt","year":"2009","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1109\/JSYST.2015.2433294","article-title":"K-clique Communities Detection in Social Networks based on Formal Concept Analysis","volume":"11","author":"Hao","year":"2017","journal-title":"IEEE Syst. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.neucom.2015.10.141","article-title":"K-clique Mining in Dynamic Social Networks based on Triadic Formal Concept Analysis","volume":"209","author":"Hao","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.ins.2014.03.056","article-title":"Expressive generalized itemsets","volume":"278","author":"Baralis","year":"2014","journal-title":"Inform. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.1016\/j.eswa.2013.08.039","article-title":"Misleading generalized itemset discovery","volume":"41","author":"Cagliero","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.is.2012.01.005","article-title":"Mining frequent itemsets in a stream","volume":"39","author":"Calders","year":"2014","journal-title":"Inform. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1016\/j.datak.2009.05.001","article-title":"Sweeping the disjunctive search space towards mining new exact concise representations of frequent itemsets","volume":"68","author":"Hamrouni","year":"2009","journal-title":"Data Knowledge Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Imielinski, T., and Swami, A. (1993, January 25\u201328). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, USA.","DOI":"10.1145\/170035.170072"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10618-006-0059-1","article-title":"Frequent pattern mining: Current status and future directions","volume":"15","author":"Han","year":"2007","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_17","first-page":"147","article-title":"An efficient algorithm for mining frequent maximal and closed itemsets","volume":"6","author":"Gharib","year":"2009","journal-title":"Int. J. Hybrid Intell. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1109\/TKDE.2005.166","article-title":"Fast Algorithms for Frequent Itemset Mining Using FP-Trees","volume":"17","author":"Grahne","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1109\/TKDE.2005.60","article-title":"Efficient algorithms for mining closed itemsets and their lattice structure","volume":"17","author":"Zaki","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.knosys.2013.02.003","article-title":"A lattice-based approach for mining most generalization association rules","volume":"45","author":"Vo","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1016\/j.eswa.2012.08.065","article-title":"A new method for mining Frequent Weighted Itemsets based on WIT-trees","volume":"40","author":"Vo","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1109\/TKDE.2012.59","article-title":"Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases","volume":"25","author":"Tseng","year":"2013","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ins.2013.02.027","article-title":"Formal concept analysis based on the topology for attributes of a formal context","volume":"236","author":"Pei","year":"2013","journal-title":"Inform. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.knosys.2014.04.017","article-title":"Neighborhood systems and covering approximation spaces","volume":"66","author":"Syau","year":"2014","journal-title":"Knowl.-Based Syst."},{"key":"ref_25","first-page":"34","article-title":"On covering rough sets","volume":"Volume 4481","author":"Yao","year":"2007","journal-title":"Rough Sets and Knowledge Technology, Lecture Notes in Artificial Intelligence"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.ins.2008.09.015","article-title":"Relationship between generalized rough sets based on binary relation and covering","volume":"179","author":"Zhu","year":"2009","journal-title":"Inform. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.neucom.2015.12.142","article-title":"Iceberg Clique Queries in Large Graphs","volume":"256","author":"Hao","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8:1","DOI":"10.1145\/2981545","article-title":"Using Centrality Measures to Predict Helpfulness-Based Reputation in Trust Networks","volume":"17","author":"Meo","year":"2017","journal-title":"ACM Trans. Int. Technol."},{"key":"ref_29","unstructured":"Golbeck, J., Parsia, B., and Hendler, J. (2013). Trust networks on the semantic web. Cooperative Information Agents VII, Springer."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jamali, M., and Abolhassani, H. (2006, January 18\u201322). Different aspects of social network analysis. Proceedings of the 2006 IEEE\/WIC\/ACM International Conference on Web Intelligence (WI 2006), Hong Kong, China.","DOI":"10.1109\/WI.2006.61"},{"key":"ref_31","unstructured":"Ghosh, A., Mahdian, M., Reeves, D.M., Pennock, D.M., and Fugger, R. (2007, January 12\u201314). Mechanism Design on Trust Networks. Proceedings of the 3rd International Workshop on Web and Internet Economics, San Diego, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1109\/TII.2017.2684807","article-title":"An Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things","volume":"13","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, Z., and He, Y. (2013, January 7\u20138). Collaborative Filtering Recommendation Algorithm Based on Users of Maximum Similar Clique. Proceedings of the 2013 International Conference on Information Science and Cloud Computing Companion, Guangzhou, China.","DOI":"10.1109\/ISCC-C.2013.58"},{"key":"ref_34","first-page":"1","article-title":"Evolutionary Graph Clustering for Protein Complex Identification","volume":"PP","author":"He","year":"2016","journal-title":"IEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_35","unstructured":"(2017, June 17). The Dataset of Collaborations between Scientists. Available online: http:\/\/www-personal.umich.edu\/~mejn\/netdata\/."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/7\/100\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:40:51Z","timestamp":1760208051000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/7\/100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,29]]},"references-count":35,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2017,7]]}},"alternative-id":["sym9070100"],"URL":"https:\/\/doi.org\/10.3390\/sym9070100","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2017,6,29]]}}}