{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:22:51Z","timestamp":1771064571058,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T00:00:00Z","timestamp":1667606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072160"],"award-info":[{"award-number":["62072160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["212102310381"],"award-info":[{"award-number":["212102310381"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"key scientific and technical project of Henan Province","award":["62072160"],"award-info":[{"award-number":["62072160"]}]},{"name":"key scientific and technical project of Henan Province","award":["212102310381"],"award-info":[{"award-number":["212102310381"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The overlapping community detection algorithm divides social networks into multiple overlapping parts, and members can belong to multiple communities at the same time. Although the overlapping community detection algorithm can help people understand network topology, it exposes personal privacy. The BIH algorithm is proposed to solve the problem of personal privacy leaks in overlapping areas. However, some specific members in overlapping areas do not want to be discovered to belong to some specific community. To solve this problem, an overlapping community hiding algorithm based on multi level neighborhood information (MLNI) is proposed. The MLNI algorithm defines node probability of community based on multi-layer neighborhood information. By adjusting the probability of the target node belonging to each community, the difference between the probability that the target node belongs to outside and inside the target community is maximized. This process can be regarded as an optimization problem. In addition, the MLNI algorithm uses the genetic algorithm to find the optimal solution, and finally achieves the purpose of moving the target node in the overlapping area out of a specific community. The effectiveness of the MLNI algorithm is demonstrated through extensive experiments and baseline algorithms. The MLNI algorithm effectively realizes the protection of personal privacy in social networks.<\/jats:p>","DOI":"10.3390\/sym14112328","type":"journal-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T03:10:46Z","timestamp":1667790646000},"page":"2328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Overlapping Community Hiding Method Based on Multi-Level Neighborhood Information"],"prefix":"10.3390","volume":"14","author":[{"given":"Guoliang","family":"Yang","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Henan Normal University, Xinxiang 453000, China"}]},{"given":"Yanwei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan Normal University, Xinxiang 453000, China"}]},{"given":"Zhengchao","family":"Chang","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Henan Institute of Technology, Xinxiang 453000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4346-9565","authenticated-orcid":false,"given":"Dong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan Normal University, Xinxiang 453000, China"},{"name":"Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, Xinxiang 453000, China"},{"name":"Big Data Engineering Lab of Teaching Resources & Assessment of Education Quality, Xinxiang 453000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1109\/TCYB.2017.2696998","article-title":"Social Synchrony on Complex Networks","volume":"48","author":"Xuan","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xuan, Q., Fang, H., Fu, C., and Filkov, V. (2015). Temporal motifs reveal collaboration patterns in online task-oriented networks. Phys. Rev. E, 91.","DOI":"10.1103\/PhysRevE.91.052813"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2826","DOI":"10.1016\/j.comcom.2007.05.024","article-title":"A survey on clustering algorithms for wireless sensor networks","volume":"30","author":"Abbasi","year":"2007","journal-title":"Comput. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s41109-020-00289-9","article-title":"A comparative study of overlapping community detection methods from the perspective of the structural properties","volume":"5","author":"Vieira","year":"2020","journal-title":"Appl. Netw. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1109\/JPROC.2017.2786710","article-title":"Applications of Community Detection Techniques to Brain Graphs: Algorithmic Considerations and Implications for Neural Function","volume":"106","author":"Garcia","year":"2018","journal-title":"Proc. IEEE"},{"key":"ref_6","first-page":"115","article-title":"Robustness of Interdependent Power Grids and Communication Networks: A Complex Network Perspective","volume":"65","author":"Chen","year":"2018","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1080\/14697680902882420","article-title":"International trade and financial integration: A weighted network analysis","volume":"10","author":"Schiavo","year":"2010","journal-title":"Quant. Financ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gross, R., and Acquisti, A. (2015, January 7). Information Revelation and Privacy in Online Social Networks. Proceedings of the 2005 ACM Workshop on Privacy in the Electronic Society (WPES\u201905), Alexandria, VA, USA.","DOI":"10.1145\/1102199.1102214"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/MNET.2010.5510913","article-title":"Privacy and security for online social networks: Challenges and opportunities","volume":"24","author":"Zhang","year":"2010","journal-title":"IEEE Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1145\/1540276.1540279","article-title":"A brief survey on anonymization techniques for privacy preserving publishing of social network data","volume":"10","author":"Zhou","year":"2008","journal-title":"ACM Sigkdd Explor. Newsl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1073\/pnas.1510612113","article-title":"Private algorithms for the protected in social network search","volume":"113","author":"Kearns","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1038\/s41562-017-0290-3","article-title":"Hiding Individuals and Communities in a Social Network","volume":"2","author":"Waniek","year":"2018","journal-title":"Nat. Hum. Behav."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/TKDE.2017.2776133","article-title":"Community Deception or: How to Stop Fearing Community Detection Algorithms","volume":"30","author":"Fionda","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TCSS.2019.2912801","article-title":"GA-Based Q-Attack on Community Detection","volume":"6","author":"Chen","year":"2019","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_15","first-page":"12938","article-title":"Rem: From structural entropy to community structure deception","volume":"32","author":"Liu","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TCSS.2020.3031596","article-title":"Multiscale Evolutionary Perturbation Attack on Community Detection","volume":"8","author":"Chen","year":"2021","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladeni\u010d, D., and Skowron, A. (2007, January 17\u201321). An Algorithm to Find Overlapping Community Structure in Networks. Proceedings of the Knowledge Discovery in Databases: PKDD 2007, Warsaw, Poland.","DOI":"10.1007\/978-3-540-74976-9"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"43:1","DOI":"10.1145\/2501654.2501657","article-title":"Overlapping community detection in networks: The state-of-the-art and comparative study","volume":"45","author":"Xie","year":"2013","journal-title":"ACM Comput. Surv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1109\/TBDATA.2022.3152431","article-title":"How to Protect Ourselves From Overlapping Community Detection in Social Networks","volume":"8","author":"Liu","year":"2022","journal-title":"IEEE Trans. Big Data"},{"key":"ref_20","unstructured":"Becker, M. (2018, January 26\u201327). Community Detection in Complex Networks using Genetic Algorithms. Proceedings of the SKILL 2018\u2014Studierendenkonferenz Informatik, Berlin, Germany."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"50","DOI":"10.3141\/2214-07","article-title":"Application of complex network theory and genetic algorithm in airline route networks","volume":"2214","author":"Liu","year":"2011","journal-title":"Transp. Res. Rec."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"384","DOI":"10.3923\/itj.2012.384.387","article-title":"Community detection via improved genetic algorithm in complex network","volume":"11","author":"Wang","year":"2012","journal-title":"Inf. Technol. J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, D., Duan, D., Shikai, S., and Song, G. (2015). Effective Semisupervised Community Detection Using Negative Information. Math. Probl. Eng., 2015.","DOI":"10.1155\/2015\/109671"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.physa.2014.08.051","article-title":"Semi-supervised community detection based on discrete potential theory","volume":"416","author":"Liu","year":"2014","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, D., Bai, H.Y., Li, H.J., and Wang, W.J. (2014). Semi-supervised community detection using label propagation. Int. J. Mod. Phys. B, 28.","DOI":"10.1142\/S0217979214502087"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"37261","DOI":"10.1109\/ACCESS.2018.2838568","article-title":"Semi-Supervised Community Detection Based on Distance Dynamics","volume":"6","author":"Fan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, D., Wang, C., and Jing, Y. (2016). Estimating the optimal number of communities by cluster analysis. Int. J. Mod. Phys. B, 30.","DOI":"10.1142\/S0217979216500375"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, D., Chang, Z., Yang, G., and Chen, E. (2022). Community hiding using a graph autoencoder. Knowl.-Based Syst., 253.","DOI":"10.1016\/j.knosys.2022.109495"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yolum, P., G\u00fcng\u00f6r, T., G\u00fcrgen, F., and \u00d6zturan, C. (2005, January 26\u201328). Computing Communities in Large Networks Using Random Walks. Proceedings of the Computer and Information Sciences\u2014ISCIS 2005, Istanbul, Turkey.","DOI":"10.1007\/11569596"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1073\/pnas.0706851105","article-title":"Maps of random walks on complex networks reveal community structure","volume":"105","author":"Rosvall","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_32","first-page":"66","article-title":"Finding community structure in very large networks","volume":"6","author":"Clauset","year":"2004","journal-title":"Phys. Rev. E Stat. Nonliner Soft Matter Phys."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Reichardt, J., and Bornholdt, S. (2006). Statistical mechanics of community detection. Phys. Rev. E, 74.","DOI":"10.1103\/PhysRevE.74.016110"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gao, R., Li, S., Shi, X., Liang, Y., and Xu, D. (2021). Overlapping Community Detection Based on Membership Degree Propagation. Entropy, 23.","DOI":"10.3390\/e23010015"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Coscia, M., Rossetti, G., Giannotti, F., and Pedreschi, D. (2012, January 12\u201316). DEMON: A local-first discovery method for overlapping communities. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD\u201912), Beijing, China.","DOI":"10.1145\/2339530.2339630"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1038\/nature03607","article-title":"Uncovering the overlapping community structure of complex networks in nature and society","volume":"435","author":"Palla","year":"2005","journal-title":"Nature"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1038\/nature09182","article-title":"Link communities reveal multiscale complexity in networks","volume":"466","author":"Ahn","year":"2010","journal-title":"Nature"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1108\/LHT-01-2019-0003","article-title":"Overlapping community detection based on the union of all maximum spanning trees","volume":"38","author":"Asmi","year":"2020","journal-title":"Library Hi Tech"},{"key":"ref_39","unstructured":"Nagaraja, S. (2010, January 21\u201323). The impact of unlinkability on adversarial community detection: Effects and countermeasures. Proceedings of the 10th International Conference on Privacy Enhancing Technologies (PETS\u201910), Berlin, Germany."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/TCSS.2021.3054115","article-title":"Community Hiding by Link Perturbation in Social Networks","volume":"8","author":"Chen","year":"2021","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1007\/s00265-003-0651-y","article-title":"The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations","volume":"54","author":"Lusseau","year":"2003","journal-title":"Behav. Ecol. Sociobiol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1086\/jar.33.4.3629752","article-title":"An information flow model for conflict and fission in small groups","volume":"33","author":"Zachary","year":"1977","journal-title":"J. Anthropol. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"8577","DOI":"10.1073\/pnas.0601602103","article-title":"Modularity and community structure in networks","volume":"103","author":"Newman","year":"2006","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4165","DOI":"10.1016\/j.physa.2011.12.021","article-title":"Social structure of facebook networks","volume":"391","author":"Traud","year":"2012","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Xie, J., Szymanski, B.K., and Liu, X. (2011, January 11). SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-listener Interaction Dynamic Process. Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW), Vancouver, BC, Canada.","DOI":"10.1109\/ICDMW.2011.154"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/11\/2328\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:11:11Z","timestamp":1760145071000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/11\/2328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,5]]},"references-count":45,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["sym14112328"],"URL":"https:\/\/doi.org\/10.3390\/sym14112328","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,5]]}}}