{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:01:05Z","timestamp":1755838865848},"reference-count":43,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2019,9,1]]},"DOI":"10.1587\/transinf.2018edp7205","type":"journal-article","created":{"date-parts":[[2019,8,31]],"date-time":"2019-08-31T22:11:03Z","timestamp":1567289463000},"page":"1773-1783","source":"Crossref","is-referenced-by-count":4,"title":["Hierarchical Community Detection in Social Networks Based on Micro-Community and Minimum Spanning Tree"],"prefix":"10.1587","volume":"E102.D","author":[{"given":"Zhixiao","family":"WANG","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengnan","family":"HOU","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guan","family":"YUAN","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"HE","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"CUI","sequence":"additional","affiliation":[{"name":"Baidu Online Network Technology (Beijing) Co., Ltd"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingjun","family":"ZHU","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] Z. Wang, J. Xi, Y. Xing, and Z. Hu, \u201cCommunity number estimation for community detection in complex networks,\u201d Journal of Information Science and Engineering, vol.33, no.5, pp.1323-1341, 2017."},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] W. Zhi-Xiao, L. Ze-chao, D. Xiao-fang, and T. Jin-hui, \u201cOverlapping community detection based on node location analysis,\u201d Knowledge-Based Systems, vol.105, pp.225-235, 2016. 10.1016\/j.knosys.2016.05.024","DOI":"10.1016\/j.knosys.2016.05.024"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] W. Wang and W.N. Street, \u201cFinding hierarchical communities in complex networks using influence-guided label propagation,\u201d 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp.547-556, IEEE, 2015. 10.1109\/icdmw.2015.58","DOI":"10.1109\/ICDMW.2015.58"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] S. Zhao, C. Yu, and Y. Zhang, \u201cHierarchical community detection based on multi degrees of distance space and submodularity optimization,\u201d Chinese National Conference on Social Media Processing, pp.343-354, Springer, 2017. 10.1007\/978-981-10-6805-8_28","DOI":"10.1007\/978-981-10-6805-8_28"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] G. Cai, R. Wang, and G. Liu, \u201cHierarchical overlapping community discovery algorithm based on node purity,\u201d International Conference on Intelligent Information Processing, pp.248-257, Springer, 2012. 10.1007\/978-3-642-32891-6_32","DOI":"10.1007\/978-3-642-32891-6_32"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] D. Guohui, S. Huimin, F. Chunlong, and S. Yan, \u201cCommunity detection algorithm of the large-scale complex networks based on random walk,\u201d International Conference on Web-Age Information Management, pp.269-282, Springer, 2016. 10.1007\/978-3-319-47121-1_23","DOI":"10.1007\/978-3-319-47121-1_23"},{"key":"7","unstructured":"[7] M. Shirzad and M.R. Feizi-Derakhshi, \u201cHierarchical community detection in social networks using spectral method,\u201d International Journal of Computer Science and Information Security, vol.14, no.8, pp.51-59, 2016."},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] M.E.J. Newman, \u201cFast algorithm for detecting community structure in networks,\u201d Physical review E, vol.69, no.6, p.066133, 2004. 10.1103\/physreve.69.066133","DOI":"10.1103\/PhysRevE.69.066133"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] A. Clauset, M.E.J. Newman, and C. Moore, \u201cFinding community structure in very large networks,\u201d Physical review E, vol.70, no.6, p.066111, 2004. 10.1103\/physreve.70.066111","DOI":"10.1103\/PhysRevE.70.066111"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] A.M. Fiscarelli, A. Beliakov, S. Konchenko, and P. Bouvry, \u201cA degenerate agglomerative hierarchical clustering algorithm for community detection,\u201d Asian Conference on Intelligent Information and Database Systems, pp.234-242, Springer, 2018. 10.1007\/978-3-319-75417-8_22","DOI":"10.1007\/978-3-319-75417-8_22"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] B. Saoud and A. Moussaoui, \u201cA new hierarchical method to find community structure in networks,\u201d Physica A: Statistical Mechanics and its Applications, vol.495, pp.418-426, 2018. 10.1016\/j.physa.2017.12.095","DOI":"10.1016\/j.physa.2017.12.095"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] R. Franke, \u201cChimera: Top-down model for hierarchical, overlapping and directed cluster structures in directed and weighted complex networks,\u201d Physica A: Statistical Mechanics and its Applications, vol.461, pp.384-408, 2016. 10.1016\/j.physa.2016.05.063","DOI":"10.1016\/j.physa.2016.05.063"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] R. Toujani and J. Akaichi, \u201cOptimal initial partitionning for high quality hybrid hierarchical community detection in social networks,\u201d 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT), pp.0395-0403, IEEE, 2017. 10.1109\/codit.2017.8102624","DOI":"10.1109\/CoDIT.2017.8102624"},{"key":"14","unstructured":"[14] R. Toujani and J. Akaichi, \u201cA model based metaheuristic for hybrid hierarchical community structure in social networks,\u201d World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol.11, no.6, pp.661-666, 2017."},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] M.J. Barber, \u201cDetecting hierarchical communities in networks: A new approach,\u201d Stochastic and Infinite Dimensional Analysis, pp.19-37, Springer, 2016. 10.1007\/978-3-319-07245-6_2","DOI":"10.1007\/978-3-319-07245-6_2"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] C.-C. Lin, J.-R. Kang, and J.-Y. Chen, \u201cAn integer programming approach and visual analysis for detecting hierarchical community structures in social networks,\u201d Information Sciences Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, vol.299, pp.296-311, 2015. 10.1016\/j.ins.2014.12.009","DOI":"10.1016\/j.ins.2014.12.009"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] J. Huang, H. Sun, J. Han, and B. Feng, \u201cDensity-based shrinkage for revealing hierarchical and overlapping community structure in networks,\u201d Physica A: Statistical Mechanics and its Applications, vol.390, no.11, pp.2160-2171, 2011. 10.1016\/j.physa.2010.10.040","DOI":"10.1016\/j.physa.2010.10.040"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] N. Schlitter, T. Falkowski, et al., \u201cDengraph-ho: Density-based hierarchical community detection for explorative visual network analysis,\u201d International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp.283-296, Springer, 2011.","DOI":"10.1007\/978-1-4471-2318-7_22"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] K. Subramani, A. Velkov, I. Ntoutsi, P. Kroger, and H.-P. Kriegel, \u201cDensity-based community detection in social networks,\u201d 2011 IEEE 5th International Conference on Internet Multimedia Systems Architecture and Application, pp.1-8, IEEE, 2011. 10.1109\/imsaa.2011.6156357","DOI":"10.1109\/IMSAA.2011.6156357"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] X. Qi, W. Tang, Y. Wu, G. Guo, E. Fuller, and C.-Q. Zhang, \u201cOptimal local community detection in social networks based on density drop of subgraphs,\u201d Pattern Recognition Letters, vol.36, no.1, pp.46-53, 2014. 10.1016\/j.patrec.2013.09.008","DOI":"10.1016\/j.patrec.2013.09.008"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] P. Kim and S. Kim, \u201cDetecting overlapping and hierarchical communities in complex network using interaction-based edge clustering,\u201d Physica A: Statistical Mechanics and its Applications, vol.417, pp.46-56, 2015. 10.1016\/j.physa.2014.09.035","DOI":"10.1016\/j.physa.2014.09.035"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] L. Zhou, K. L\u00fc, P. Yang, L. Wang, and B. Kong, \u201cAn approach for overlapping and hierarchical community detection in social networks based on coalition formation game theory,\u201d Expert Systems with Applications, vol.42, no.24, pp.9634-9646, 2015. 10.1016\/j.eswa.2015.07.023","DOI":"10.1016\/j.eswa.2015.07.023"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] L. Zhou, C. Cheng, K. L\u00fc, and H. Chen, \u201cUsing coalitional games to detect communities in social networks,\u201d International Conference on Web-Age Information Management, pp.326-331, Springer, 2013. 10.1007\/978-3-642-38562-9_33","DOI":"10.1007\/978-3-642-38562-9_33"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] W. Zhang, F. Kong, L. Yang, Y. Chen, and M. Zhang,\u201cHierarchical community detection based on partial matrix convergence using random walks,\u201d Tsinghua Science and Technology, vol.23, no.1, pp.35-46, 2018. 10.26599\/tst.2018.9010053","DOI":"10.26599\/TST.2018.9010053"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] J. Qiu and Z. Lin, \u201cD-hocs: an algorithm for discovering the hierarchical overlapping community structure of a social network,\u201d Journal of Intelligent Information Systems, vol.42, no.3, pp.353-370, 2014. 10.1007\/s10844-013-0272-5","DOI":"10.1007\/s10844-013-0272-5"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] B. Yang, J. Di, J. Liu, and D. Liu, \u201cHierarchical community detection with applications to real-world network analysis,\u201d Data & Knowledge Engineering, vol.83, no.4, pp.20-38, 2013. 10.1016\/j.datak.2012.09.002","DOI":"10.1016\/j.datak.2012.09.002"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] R. Du, D. Kuang, B. Drake, and H. Park, \u201cHierarchical community detection via rank-2 symmetric nonnegative matrix factorization,\u201d Computational social networks, vol.4, no.1, p.7, 2017. 10.1186\/s40649-017-0043-5","DOI":"10.1186\/s40649-017-0043-5"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] N. Gillis, D. Kuang, and H. Park, \u201cHierarchical clustering of hyperspectral images using rank-two nonnegative matrix factorization,\u201d IEEE Trans. Geosci. Remote Sens., vol.53, no.4, pp.2066-2078, 2015. 10.1109\/tgrs.2014.2352857","DOI":"10.1109\/TGRS.2014.2352857"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] R.K. Behera, S.K. Rath, and M. Jena, \u201cSpanning tree based community detection using min-max modularity,\u201d Procedia Computer Science, vol.93, pp.1070-1076, 2016. 10.1016\/j.procs.2016.07.311","DOI":"10.1016\/j.procs.2016.07.311"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] B. Saoud and A. Moussaoui, \u201cCommunity detection in networks based on minimum spanning tree and modularity,\u201d Physica A: Statistical Mechanics and its Applications, vol.460, pp.230-234, 2016. 10.1016\/j.physa.2016.05.014","DOI":"10.1016\/j.physa.2016.05.014"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] K. Asmi, D. Lotfi, and M. El Marraki, \u201cA novel approach based on the minimum spanning tree to discover communities in social networks,\u201d 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), pp.286-290, IEEE, 2016. 10.1109\/wincom.2016.7777228","DOI":"10.1109\/WINCOM.2016.7777228"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] M. Girvan and M.E.J. Newman, \u201cCommunity structure in social and biological networks,\u201d Proc. national academy of sciences, vol.99, no.12, pp.7821-7826, 2002. 10.1073\/pnas.122653799","DOI":"10.1073\/pnas.122653799"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] A. Lancichinetti, S. Fortunato, and F. Radicchi, \u201cBenchmark graphs for testing community detection algorithms,\u201d Physical review E, vol.78, no.4, p.046110, 2008. 10.1103\/physreve.78.046110","DOI":"10.1103\/PhysRevE.78.046110"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] A. Arenas, A. D\u00edaz-Guilera, and C.J. P\u00e9rez-Vicente, \u201cSynchronization reveals topological scales in complex networks,\u201d Physical review letters, vol.96, no.11, p.114102, 2006. 10.1103\/physrevlett.96.114102","DOI":"10.1103\/PhysRevLett.96.114102"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] M.E.J. Newman and M. Girvan, \u201cFinding and evaluating community structure in networks,\u201d Physical review E, vol.69, no.2, p.026113, 2004. 10.1103\/physreve.69.026113","DOI":"10.1103\/PhysRevE.69.026113"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] W.W. Zachary, \u201cAn information flow model for conflict and fission in small groups,\u201d Journal of anthropological research, vol.33, no.4, pp.452-473, 1977. 10.1086\/jar.33.4.3629752","DOI":"10.1086\/jar.33.4.3629752"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] M.E.J. Newman, \u201cModularity and community structure in networks,\u201d Proc. national academy of sciences, vol.103, no.23, pp.8577-8582, 2006. 10.1073\/pnas.0601602103","DOI":"10.1073\/pnas.0601602103"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] Y. Liu, H. Gao, X. Kang, Q. Liu, R. Wang, and Z. Qin, \u201cFast community discovery and its evolution tracking in time-evolving social networks,\u201d 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp.13-20, IEEE, 2015. 10.1109\/icdmw.2015.177","DOI":"10.1109\/ICDMW.2015.177"},{"key":"39","doi-asserted-by":"crossref","unstructured":"[39] D. Bu, Y. Zhao, L. Cai, H. Xue, X. Zhu, H. Lu, J. Zhang, S. Sun, L. Ling, N. Zhang, G. Li, and R. Chen, \u201cTopological structure analysis of the protein-protein interaction network in budding yeast,\u201d Nucleic acids research, vol.31, no.9, pp.2443-2450, 2003. 10.1093\/nar\/gkg340","DOI":"10.1093\/nar\/gkg340"},{"key":"40","unstructured":"[40] J. Leskovec and J.J. Mcauley, \u201cLearning to discover social circles in ego networks,\u201d Advances in Neural Information Processing Systems, pp.539-547, 2012."},{"key":"41","doi-asserted-by":"publisher","unstructured":"[41] D.J. Watts and S.H. Strogatz, \u201cCollective dynamics of \u2018small-world\u2019 networks,\u201d nature, vol.393, no.6684, pp.440-442, 1998. 10.1038\/30918","DOI":"10.1038\/30918"},{"key":"42","doi-asserted-by":"publisher","unstructured":"[42] J. Leskovec, J. Kleinberg, and C. Faloutsos, \u201cGraph evolution: Densification and shrinking diameters,\u201d ACM Transactions on Knowledge Discovery from Data (TKDD), vol.1, no.1, p.2, 2007. 10.1145\/1217299.1217301","DOI":"10.1145\/1217299.1217301"},{"key":"43","doi-asserted-by":"crossref","unstructured":"[43] C. Giatsidis, F.D. Malliaros, D.M. Thilikos, and M. Vazirgiannis, \u201cCorecluster: A degeneracy based graph clustering framework,\u201d AAAI, pp.44-50, 2014.","DOI":"10.1609\/aaai.v28i1.8731"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/9\/E102.D_2018EDP7205\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T03:04:32Z","timestamp":1664247872000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/9\/E102.D_2018EDP7205\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,1]]},"references-count":43,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2019]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2018edp7205","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,1]]}}}