{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T01:48:35Z","timestamp":1768096115528,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,8]],"date-time":"2022-01-08T00:00:00Z","timestamp":1641600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171180, 62072158"],"award-info":[{"award-number":["62171180, 62072158"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Key Science and Research Program at the University of Henan Province","award":["21A510001"],"award-info":[{"award-number":["21A510001"]}]},{"name":"Program for Innovative Research Team in University of Henan Province","award":["21IRTSTHN015"],"award-info":[{"award-number":["21IRTSTHN015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Identifying offline entities corresponding to multiple virtual accounts of users across social networks is crucial for the development of related fields, such as user recommendation system, network security, and user behavior pattern analysis. The data generated by users on multiple social networks has similarities. Thus, the concept of symmetry can be used to analyze user-generated information for user identification. In this paper, we propose a friendship networks-based user identification across social networks algorithm (FNUI), which performs the similarity of multi-hop neighbor nodes of a user to characterize the information redundancy in the friend networks fully. Subsequently, a gradient descent algorithm is used to optimize the contribution of the user\u2019s multi-hop nodes in the user identification process. Ultimately, user identification is achieved in conjunction with the Gale\u2013Shapley matching algorithm. Experimental results show that compared with baselines, such as friend relationship-based user identification (FRUI) and friendship learning-based user identification (FBI): (1) The contribution of single-hop neighbor nodes in the user identification process is higher than other multi-hop neighbor nodes; (2) The redundancy of information contained in multi-hop neighbor nodes has a more significant impact on user identification; (3) The precision rate, recall rate, comprehensive evaluation index (F1), and area under curve (AUC) of user identification have been improved.<\/jats:p>","DOI":"10.3390\/sym14010110","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:35:09Z","timestamp":1641771309000},"page":"110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Exploiting User Friendship Networks for User Identification across Social Networks"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3614-3321","authenticated-orcid":false,"given":"Yating","family":"Qu","sequence":"first","affiliation":[{"name":"School of Automotive and Rail Transportation, Luoyang Polytechnic, Luoyang 471099, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5132-3817","authenticated-orcid":false,"given":"Ling","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0291-3001","authenticated-orcid":false,"given":"Huahong","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0209-4488","authenticated-orcid":false,"given":"Honghai","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3503-2574","authenticated-orcid":false,"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automotive and Rail Transportation, Luoyang Polytechnic, Luoyang 471099, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1123-6978","authenticated-orcid":false,"given":"Kaikai","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, F., Song, X., Song, Y.I., Lin, C.Y., and Hon, H.W. (2013, January 4\u20138). What\u2019s in a name? An unsupervised approach to link users across communities. Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, Rome, Italy.","DOI":"10.1145\/2433396.2433457"},{"key":"ref_2","unstructured":"(2021, December 05). Most Popular Social Networks Worldwide as of July 2021, Ranked by Number of Active Users [EB\/OL]. Available online: https:\/\/www.statista.com\/statistics\/272014\/global-social-networks-ranked-by-number-of-users\/."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zheng, J., Li, D., and Arun Kumar, S. (2018). Group user profile modeling based on neural word embeddings in social networks. Symmetry, 10.","DOI":"10.3390\/sym10100435"},{"key":"ref_4","first-page":"905","article-title":"Review of User Identification across Social Networks: The Complex Network Approach","volume":"49","author":"Xing","year":"2020","journal-title":"J. Univ. Electron. Sci. Technol. China"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, C.Y., and Lin, S.D. (2014, January 24\u201327). Matching users and items across domains to improve the recommendation quality. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2623330.2623657"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.neucom.2015.10.147","article-title":"Identifying users across social networks based on dynamic core interests","volume":"210","author":"Nie","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"137472","DOI":"10.1109\/ACCESS.2019.2942840","article-title":"A survey of across social networks user identification","volume":"7","author":"Xing","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17342","DOI":"10.1109\/ACCESS.2017.2744646","article-title":"User identification based on display names across online social networks","volume":"5","author":"Li","year":"2017","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1082391","DOI":"10.1155\/2021\/1082391","article-title":"Exploiting Two-Level Information Entropy across Social Networks for User Identification","volume":"2021","author":"Xing","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1145\/3068777.3068781","article-title":"User identity linkage across online social networks: A review","volume":"18","author":"Shu","year":"2017","journal-title":"ACM Sigkdd Explor. Newsl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"47114","DOI":"10.1109\/ACCESS.2019.2909089","article-title":"A user identification algorithm based on user behavior analysis in social networks","volume":"7","author":"Deng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xing, L., Deng, K., Wu, H., Xie, P., and Gao, J. (2019). Behavioral habits-based user identification across social networks. Symmetry, 11.","DOI":"10.3390\/sym11091134"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1109\/TKDE.2015.2485222","article-title":"Cross-platform identification of anonymous identical users in multiple social media networks","volume":"28","author":"Zhou","year":"2015","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mishra, R. (2019). Entity resolution in online multiple social networks (@ Facebook and LinkedIn). Emerging Technologies in Data Mining and Information Security, Springer.","DOI":"10.1007\/978-981-13-1498-8_20"},{"key":"ref_15","unstructured":"Zafarani, R., and Liu, H. (2009, January 17\u201320). Connecting corresponding identities across communities. Proceedings of the Third International AAAI Conference on Weblogs and Social Media, San Jose, CA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Perito, D., Castelluccia, C., Kaafar, M.A., and Manils, P. (2011). How unique and traceable are usernames?. International Symposium on Privacy Enhancing Technologies Symposium, Springer.","DOI":"10.1007\/978-3-642-22263-4_1"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.comnet.2019.04.015","article-title":"Smpft: Social media based profile fusion technique for data enrichment","volume":"158","author":"Agarwal","year":"2019","journal-title":"Comput. Netw."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Motoyama, M., and Varghese, G. (2009, January 5\u20139). I seek you: Searching and matching individuals in social networks. Proceedings of the Eleventh International Workshop on Web Information and Data Management, Marina Del Rey, CA, USA.","DOI":"10.1145\/1651587.1651604"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Narayanan, A., and Shmatikov, V. (2009, January 17\u201320). De-anonymizing social networks. Proceedings of the 2009 30th IEEE Symposium on Security and Privacy, Berkeley, CA, USA.","DOI":"10.1109\/SP.2009.22"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Raad, E., Chbeir, R., and Dipanda, A. (2010, January 14\u201316). User profile matching in social networks. Proceedings of the 2010 13th International Conference on Network-Based Information Systems, Takayama, Japan.","DOI":"10.1109\/NBiS.2010.35"},{"key":"ref_21","unstructured":"Bartunov, S., Korshunov, A., Park, S.T., Ryu, W., and Lee, H. (2012, January 26\u201329). Joint link-attribute user identity resolution in online social networks. Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining, Workshop on Social Network Mining and Analysis, Istanbul, Turkey."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Narayanan, A., Paskov, H., Gong, N.Z., Bethencourt, J., Stefanov, E., Shin, E.C.R., and Song, D. (2012). On the feasibility of internet-scale author identification. 2012 IEEE Symposium on Security and Privacy, IEEE.","DOI":"10.1109\/SP.2012.46"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zafarani, R., and Liu, H. (2013, January 11\u201314). Connecting users across social media sites: A behavioral-modeling approach. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA.","DOI":"10.1145\/2487575.2487648"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Goga, O., Lei, H., Parthasarathi, S.H.K., Friedland, G., Sommer, R., and Teixeira, R. (2013, January 13\u201317). Exploiting innocuous activity for correlating users across sites. Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil.","DOI":"10.1145\/2488388.2488428"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.future.2018.01.041","article-title":"Matching user accounts based on user generated content across social networks","volume":"83","author":"Li","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, S., Wang, S., Zhu, F., Zhang, J., and Krishnan, R. (2014, January 22\u201327). Hydra: Large-scale social identity linkage via heterogeneous behavior modeling. Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, Snowbird, UT, USA.","DOI":"10.1145\/2588555.2588559"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, H., Yin, H., Sun, X., Chen, T., Gabrys, B., and Musial, K.C. (2020, January 6\u201310). Multi-level graph convolutional networks for cross-platform anchor link prediction. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, CA, USA.","DOI":"10.1145\/3394486.3403201"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Vosecky, J., Hong, D., and Shen, V.Y. (2009, January 28\u201331). User identification across multiple social networks. Proceedings of the 2009 First International Conference on Networked Digital Technologies, Ostrava, Czech Republic.","DOI":"10.1109\/NDT.2009.5272173"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Philip, S.Y. (2015, January 14\u201317). Multiple anonymized social networks alignment. Proceedings of the 2015 IEEE International Conference on Data Mining, Atlantic City, NJ, USA.","DOI":"10.1109\/ICDM.2015.114"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1109\/TKDE.2017.2784430","article-title":"Structure based user identification across social networks","volume":"30","author":"Zhou","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1109\/TKDE.2018.2828812","article-title":"A Comment on \u2018Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks\u2019","volume":"30","author":"Li","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.ins.2019.08.022","article-title":"Exploiting similarities of user friendship networks across social networks for user identification","volume":"506","author":"Li","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Qu, Y., Yu, S., Zhou, W., and Niu, J. (2018, January 9\u201313). FBI: Friendship learning-based user identification in multiple social networks. Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/GLOCOM.2018.8647771"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"12397","DOI":"10.1007\/s00521-021-05860-8","article-title":"Privacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rules","volume":"33","author":"Reza","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3199","DOI":"10.1109\/LCOMM.2021.3072671","article-title":"A Location Privacy Protection Algorithm Based on Double K-anonymity in the Social Internet of Vehicles","volume":"25","author":"Xing","year":"2021","journal-title":"IEEE Commun. Lett."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/1\/110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:13:12Z","timestamp":1760364792000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/1\/110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,8]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["sym14010110"],"URL":"https:\/\/doi.org\/10.3390\/sym14010110","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,8]]}}}