{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T07:14:10Z","timestamp":1765610050830,"version":"3.37.3"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T00:00:00Z","timestamp":1596758400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T00:00:00Z","timestamp":1596758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of the People\u2019s Republic of China","doi-asserted-by":"publisher","award":["K&D2018YFB1004704"],"award-info":[{"award-number":["K&D2018YFB1004704"]}],"id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1736106"],"award-info":[{"award-number":["U1736106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based on the similarity scores. However, most of these methods fail to utilize the results of previously matched identities, which could contribute to the subsequent linkages in following matching steps. To address this problem, we transform user identity linkage into a sequence decision problem and propose a reinforcement learning model to optimize the linkage strategy from the global perspective. Our method makes full use of both the social network structure and the history matched identities, meanwhile explores the long-term influence of processing matching on subsequent decisions. We conduct extensive experiments on real-world datasets, the results show that our method outperforms the state-of-the-art methods.<\/jats:p>","DOI":"10.1007\/s11280-020-00833-8","type":"journal-article","created":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T17:05:47Z","timestamp":1596819947000},"page":"85-103","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["RLINK: Deep reinforcement learning for user identity linkage"],"prefix":"10.1007","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8052-4036","authenticated-orcid":false,"given":"Xiaoxue","family":"Li","sequence":"first","affiliation":[]},{"given":"Yanan","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yanmin","family":"Shang","sequence":"additional","affiliation":[]},{"given":"Yangxi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yanbing","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,7]]},"reference":[{"key":"833_CR1","unstructured":"Arora, S., Liang, Y., Ma, T.: A simple but tough-to-beat baseline for sentence embeddings. In: The International Conference on Learning Representations (ICLR) (2017)"},{"key":"833_CR2","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural Machine Translation by Jointly Learning to Align and Translate. In: The International Conference on Learning Representations (ICLR) (2014)"},{"key":"833_CR3","doi-asserted-by":"crossref","unstructured":"Cheng, A., Zhou, C., Yang, H., Wu, J., Li, L., Tan, J., Guo, L.: Deep active learning for anchor user prediction. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 2151\u20132157 (2019)","DOI":"10.24963\/ijcai.2019\/298"},{"key":"833_CR4","doi-asserted-by":"crossref","unstructured":"Fan, Y., Zhang, Y., Hou, S., Chen, L., Ye, Y., Shi, C., Zhao, L., Xu, S.: Idev: Enhancing social coding security by cross-platform user identification between github and stack overflow. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 2272\u20132278 (2019)","DOI":"10.24963\/ijcai.2019\/315"},{"key":"833_CR5","doi-asserted-by":"crossref","unstructured":"Fang, Z., Cao, Y., Li, Q., Zhang, D., Zhang, Z., Liu, Y.: Joint Entity Linking with Deep Reinforcement Learning. In: The World Wide Web Conference (WWW), pp. 438-447 (2019)","DOI":"10.1145\/3308558.3313517"},{"key":"833_CR6","doi-asserted-by":"crossref","unstructured":"Goga, O., Lei, H., Parthasarathi, S.H.K., Friedland, G., Sommer, R., Teixeira, R.: Exploiting Innocuous Activity for Correlating Users across Sites. In: The World Wide Web Conference (WWW), pp. 447\u2013458 (2013)","DOI":"10.1145\/2488388.2488428"},{"key":"833_CR7","unstructured":"Goga, O., Perito, D., Lei, H., Teixeira, R., Sommer, R.: Large-scale correlation of accounts across social networks. University of California at Berkeley, Berkeley, California, Tech. Rep TR-13-002 (2013)"},{"key":"833_CR8","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: Node2vec: Scalable feature learning for networks. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"833_CR9","doi-asserted-by":"crossref","unstructured":"Kong, X., Zhang, J., Yu, P.S.: Inferring anchor links across multiple heterogeneous social networks. In: Proceedings of the ACM international conference on Information & Knowledge Management (CIKM), pp. 179\u2013188 (2013)","DOI":"10.1145\/2505515.2505531"},{"key":"833_CR10","doi-asserted-by":"crossref","unstructured":"Hu, M., Peng, Y., Huang, Z., Qiu, X., Wei, F., Zhou, M.: Reinforced mnemonic reader for machine reading comprehension. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 4099\u20134106 (2018)","DOI":"10.24963\/ijcai.2018\/570"},{"key":"833_CR11","doi-asserted-by":"crossref","unstructured":"Kanezashi, H., Suzumura, T., Garcia-Gasulla, D., Oh, M.H., Matsuoka, S.: Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs. In: The IEEE International Conference on High Performance Computing (HiPC), pp. 92\u2013101 (2018)","DOI":"10.1109\/HiPC.2018.00019"},{"issue":"5","key":"833_CR12","doi-asserted-by":"publisher","first-page":"377","DOI":"10.14778\/2732269.2732274","volume":"7","author":"N Korula","year":"2014","unstructured":"Korula, N., Lattanzi, S.: An efficient reconciliation algorithm for social networks. Proceedings of the VLDB Endowment 7(5), 377\u2013388 (2014)","journal-title":"Proceedings of the VLDB Endowment"},{"key":"833_CR13","unstructured":"Labitzke, S., Taranu, I., Hartenstein, H.: What Your Friends Tell Others about You: Low Cost Linkability of Social Network Profiles. In: Proc. 5Th International ACM Workshop on Social Network Mining and Analysis, pp. 1065-1070 (2011)"},{"key":"833_CR14","unstructured":"Lacoste-Julien, S., Palla, K., Davies, A., Kasneci, G., Graepel, T., Ghahramani, Z.: Sigma: Simple greedy matching for aligning large knowledge bases. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp. 572\u2013580 (2013)"},{"key":"833_CR15","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural Architectures for Named Entity Recognition. In: The Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (HLT-NAACL), pp. 260\u2013270 (2016)","DOI":"10.18653\/v1\/N16-1030"},{"key":"833_CR16","doi-asserted-by":"crossref","unstructured":"Lample, G., Chaplot, D.S.: Playing FPS games with deep reinforcement learning. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) pp. 2140\u20132146 (2017)","DOI":"10.1609\/aaai.v31i1.10827"},{"key":"833_CR17","unstructured":"Liu, L., Cheung, W.K., Li, X., Liao, L.: Aligning users across social networks using network embedding. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1774\u20131780 (2016)"},{"key":"833_CR18","unstructured":"Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Sliver, D., Wierstra, D.: Continuous Control with Deep Reinforcement Learning. In: The International Conference on Learning Representations (ICLR) (2016)"},{"key":"833_CR19","doi-asserted-by":"crossref","unstructured":"Liu, S., Wang, S., Zhu, F., Zhang, J., Krishnan, R.: Hydra: Large-scale Social Identity Linkage via Heterogeneous Behavior Modeling. In: International Conference on Management of Data (SIGMOD), pp. 51\u201362 (2014)","DOI":"10.1145\/2588555.2588559"},{"key":"833_CR20","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1412\u20131421 (2015)","DOI":"10.18653\/v1\/D15-1166"},{"key":"833_CR21","unstructured":"Man, T., Shen, H., Liu, S., Jin, X., Cheng, X.: Predict anchor links across social networks via an embedding approach. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Vol. 16, pp. 1823\u20131829 (2016)"},{"issue":"7540","key":"833_CR22","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Shanelegg., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533 (2015)","journal-title":"Nature"},{"key":"833_CR23","doi-asserted-by":"crossref","unstructured":"Mu, X., Zhu, F., Lim, E.P., Xiao, J., Wang, J., Zhou, Z.H.: User identity linkage by latent user space modelling. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp. 1775\u20131784 (2016)","DOI":"10.1145\/2939672.2939849"},{"issue":"20","key":"833_CR24","first-page":"173","volume":"17","author":"A Narayanan","year":"2009","unstructured":"Narayanan, A., Shmatikov, V.: De-anonymizing social networks. IEEE symposium on security and privacy 17(20), 173\u2013187 (2009)","journal-title":"IEEE symposium on security and privacy"},{"key":"833_CR25","doi-asserted-by":"crossref","unstructured":"Peled, O., Fire, M., Rokach, L., Elovici, Y.: Entity Matching in Online Social Networks. In: International Conference on Social Computing (Socialcom), pp. 339\u2013344 (2013)","DOI":"10.1109\/SocialCom.2013.53"},{"key":"833_CR26","doi-asserted-by":"crossref","unstructured":"Peyravi, F., Derhami, V., Latif, A.: Reinforcement Learning Based Search (RLS) Algorithm in Social Networks. In: The International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 206\u2013210 (2015)","DOI":"10.1109\/AISP.2015.7123527"},{"key":"833_CR27","doi-asserted-by":"crossref","unstructured":"Riederer, C., Kim, Y., Chaintreau, A., Korula, N., Lattanzi, S.: Linking Users across Domains with Location Data: Theory and Validation. In: The World Wide Web Conference (WWW), pp. 707\u2013719 (2016)","DOI":"10.1145\/2872427.2883002"},{"key":"833_CR28","doi-asserted-by":"crossref","unstructured":"Shang, Y., Kang, Z., Cao, Y., Zhang, D., Li, Y., Li, Y., Liu, Y.: PAAE: a Unified Framework for Predicting Anchor Links with Adversarial Embedding. In: The IEEE International Conference on Multimedia and Expo (ICME), Pp. 682-687 (2019)","DOI":"10.1109\/ICME.2019.00123"},{"issue":"2","key":"833_CR29","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1145\/3068777.3068781","volume":"18","author":"K Shu","year":"2017","unstructured":"Shu, K., Wang, S., Tang, J., Zafarani, R., Liu, H.: User identity linkage across online social networks: A review. Acm Sigkdd Explorations Newsletter 18(2), 5\u201317 (2017)","journal-title":"Acm Sigkdd Explorations Newsletter"},{"issue":"7587","key":"833_CR30","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van den driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., P.Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Dieleman, S.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484\u2013489 (2016)","journal-title":"Nature"},{"key":"833_CR31","doi-asserted-by":"crossref","unstructured":"Skyrms, B., Pemantle, R.: A Dynamic Model of Social Network Formation. In: Adaptive Networks, pp. 231\u2013251 (2009)","DOI":"10.1007\/978-3-642-01284-6_11"},{"issue":"5","key":"833_CR32","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1109\/TNN.1998.712192","volume":"9","author":"RS Sutton","year":"1998","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction. IEEE Trans. Neural Networks 9(5), 1054\u20131054 (1998)","journal-title":"IEEE Trans. Neural Networks"},{"key":"833_CR33","doi-asserted-by":"crossref","unstructured":"Taghipour, N., Kardan, A.: A hybrid web recommender system based on q-learning. In: Proceedings of the 2008 ACM symposium on Applied computing (SAC), pp. 1164\u20131168 (2008)","DOI":"10.1145\/1363686.1363954"},{"key":"833_CR34","doi-asserted-by":"crossref","unstructured":"Tan, S., Guan, Z., Cai, D., Qin, X., Bu, J., Chen, C.: Mapping users across networks by manifold alignment on hypergraph. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 159\u2013165 (2014)","DOI":"10.1609\/aaai.v28i1.8720"},{"key":"833_CR35","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale Information Network Embedding. In: The World Wide Web Conference (WWW), pp. 1067\u20131077 (2015)","DOI":"10.1145\/2736277.2741093"},{"issue":"6","key":"833_CR36","doi-asserted-by":"publisher","first-page":"2611","DOI":"10.1007\/s11280-018-0572-3","volume":"22","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Feng, C., Chen, L., Yin, H., Guo, C., Chu, Y.: User identity linkage across social networks via linked heterogeneous network embedding. World Wide Web (WWWJ) 22(6), 2611\u20132632 (2019)","journal-title":"World Wide Web (WWWJ)"},{"issue":"2","key":"833_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2747880","volume":"10","author":"R Zafarani","year":"2015","unstructured":"Zafarani, R., Tang, L., Liu, H.: User identification across social media. ACM Transactions on Knowledge Discovery from Data (TKDD) 10(2), 1\u201330 (2015)","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"key":"833_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, J., Yu, P.S.: Pct: Partial Co-Alignment of Social Networks. In: The World Wide Web Conference (WWW), pp. 749\u2013759 (2016)","DOI":"10.1145\/2872427.2883038"},{"key":"833_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tang, J., Yang, Z., Pei, J., Yu, P.S.: Cosnet: Connecting heterogeneous social networks with local and global consistency. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp. 1485\u20131494 (2015)","DOI":"10.1145\/2783258.2783268"},{"key":"833_CR40","doi-asserted-by":"crossref","unstructured":"Zhao, X., Xia, L., Zhang, L., Ding, Z., Yin, D., Tang, J.: Deep reinforcement learning for page-wise recommendations (RecSys). In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 95\u2013103 (2018)","DOI":"10.1145\/3240323.3240374"},{"key":"833_CR41","doi-asserted-by":"crossref","unstructured":"Zhou, F., Liu, L., Zhang, K., Trajcevski, G., Wu, J., Zhong, T.: Deeplink: a Deep Learning Approach for User Identity Linkage. In: The IEEE Conference on Computer Communications (INFOCOM), pp. 1313\u20131321 (2018)","DOI":"10.1109\/INFOCOM.2018.8486231"},{"issue":"2","key":"833_CR42","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1109\/TKDE.2015.2485222","volume":"28","author":"X Zhou","year":"2016","unstructured":"Zhou, X., Liang, X., Zhang, H., Ma, Y.: Cross-platform identification of anonymous identical users in multiple social media networks. IEEE transactions on knowledge and data engineering 28(2), 411\u2013424 (2016)","journal-title":"IEEE transactions on knowledge and data engineering"},{"issue":"8","key":"833_CR43","doi-asserted-by":"publisher","first-page":"1786","DOI":"10.3390\/s17081786","volume":"17","author":"J Zhu","year":"2017","unstructured":"Zhu, J., Zhang, J., Wu, Q., Jia, Y., Zhou, B., Wei, X., Yu, P.S.: Constrained active learning for anchor link prediction across multiple heterogeneous social networks. Sensors 17(8), 1786 (2017)","journal-title":"Sensors"},{"key":"833_CR44","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 8697\u20138710 (2018)","DOI":"10.1109\/CVPR.2018.00907"},{"key":"833_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Fan, Y., Song, W., Hou, S., Ye, Y., Li, X., Wang, J., Xiong, Q.: Your Style Your Identity: Leveraging Writing and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network. In: The World Wide Web Conference (WWW), pp. 3448\u20133454 (2019)","DOI":"10.1145\/3308558.3313537"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-020-00833-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-020-00833-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-020-00833-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,6]],"date-time":"2022-11-06T06:33:25Z","timestamp":1667716405000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-020-00833-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,7]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["833"],"URL":"https:\/\/doi.org\/10.1007\/s11280-020-00833-8","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"type":"print","value":"1386-145X"},{"type":"electronic","value":"1573-1413"}],"subject":[],"published":{"date-parts":[[2020,8,7]]},"assertion":[{"value":"28 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}