计算机科学 ›› 2024, Vol. 51 ›› Issue (5): 12-20.doi: 10.11896/jsjkx.230300172
王庚润
WANG Gengrun
摘要： 近年来,随着移动互联网技术的发展和用户需求的增加,网络空间中各种虚拟账号越来越多,同一用户在不同应用甚至同一平台拥有多个账号。同时,由于网络空间的虚拟性导致用户的虚拟身份与真实社会身份之间的关联通常较弱,网络空间违法用户存在发现难和取证难的问题。因此,在服务推荐和调查取证等需求的推动下,以网络空间用户虚拟身份聚合和虚实身份映射为主要研究内容的用户身份对齐技术得到了快速发展。为此,对网络空间用户身份对齐技术进行了梳理,首先对该技术解决的科学问题进行了阐述;其次介绍了该技术所用到的用户身份典型特征和涉及的相关技术;然后对可供研究的数据集与验证标准进行介绍;最后对所提技术面向的应用场景进行了详细分析,并基于此讨论了用户身份对齐技术未来的研究方向以及面临的挑战。
中图分类号:
| [1]XIE L.Research on Information Investigation of Telecom Network Fraud [J].Journal of People's Public Security University of China(Science and Technology),2020,26(3):85-93. [2]ADOMAVICIUS G,TUZHILIN A.Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions [J].IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749. [3]LIU X Z,XIA T,YU Y Y,et al.Cross social media recommendation[C]//The International AAAI Conference on Web and Social Media.[S.l.]:AAAI,2016:1-10. [4]XING L,DENG K,WU H,et al.A Survey of Across Social Net-works User Identification [J].IEEE Access,2019,7:137472-137488. [5]ZAHRA K,AZAM F,BUTT W H,et al.User Identification on Social Networks Through Text Mining Techniques:A Syste-matic Literature Review[C]//International Conference on Information Science and Applications.Singapore:Springer,2019. [6]GOMAA W H,FAHMY A A.A Survey of Text Similarity Approaches [J].International Journal of Computer Applications,2013,68(13):13-18,. [7]XING L,DENG K K,WU H H,et al.Review of User Identification across Social Networks:The Complex Network Approach [J].Journal of University of Electronic Science and Technology of China,2020,49(6):905-917. [8] LUAN M M,ZHAO T,YANG X H,et al.A Survey of Cross Social Network User Identification at Home and Abroad [J].Journal of Qilu University of Technology,2020,34(4):55-60. [9]SHEN W,WANG J Y,LUO P,et al.Linking named entities in Tweets with knowledge base via user interest modeling[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2013:68-76. [10]LIU S,WANG S,ZHU F,et al.HYDRA:Large-Scale socialidentity linkage via heterogeneous behavior modeling[C]//Proceedings of the 2014 ACM SIGMOD International Conference.on Management of Data.New York:ACM Press,2014:51-62. [11]MOHAMED A,QIAN K,ELHOSEINY M,et al.Social-STGCNN:A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2020. [12]ZHOU F,LIU L,ZHANG K,et al.DeepLink:A Deep Learning Approach for User Identity Linkage[C]//IEEE INFOCOM 2018-IEEE Conference on Computer Communications.IEEE,1313-1321. [13]DENG K K,XING L,ZHENG L,et al.A User IdentificationAlgorithm Based on User Behavior Analysis in Social Networks [J].IEEE Access,2019,7:47114-47123. [14]ZAFARANI R,TANG L,LIU H.User identification across social media [J].ACM Transactions on Knowledge Discovery from Data,2015,10(2):1-30. [15]GANG H,SHA C.Implement Of Network User Virtual Identity Merging Based on Spark[C]//International Conference on Communication and Electronic Information Engineering(CEIE 2016).2017. [16]ZHOU F,GAO Q,TRAJCEVSKI G,et al.Trajectory-user lin-king via variational autoencoder[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence.Stockholm,Sweden,2018:3212-3218. [17]CHEN H C,XU Q,HUANG R Y,et al.User IdentificationAcross Social Networks Based on User Trajectory [J].Journal of Electronics & Information Technology,2018,40(11):2758-2764. [18]FU S,WANG G,XIA S,et al.A Multi-Granularity Representation Learning Framework for User Identification Across Social Networks[C]//2019 International Joint Conference on Rough Sets(IJCRS).Springer,2019. [19]XING L,DENG K K,WU H H,et al.Behavioral Habits-Based User Identification Across Social Networks [J].Symmetry,2019,11(9):1134. [20]JAMJUNTRA L,CHARTSUWAN P,WONGLIMSAMUT P,et al.Social network user identification[C]//2017 9th International Conference on Knowledge and Smart Technology(KST).IEEE,2017. [21]ZHAO D,NING Z,MING X,et al.An Improved User Identification Method Across Social Networks Via Tagging Behaviors[C]//2018 IEEE 30th International Conference on Tools with Artificial Intelligence(ICTAI).IEEE,2018. [22]GAO Q,ZHOU F,ZHANG K,et al.Identifying human mobility via trajectory embeddings[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence.Melbourne,Australia,2017:1689-1695. [23]ZHOU F,CHEN S,WU J,et al.Trajectory-User Linking via Graph Neural Network[C]//IEEE International Conference on Communications(ICC 2021).IEEE,2021. [24]ZHOU X,LIANG X,ZHANG H,et al.Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks [J].IEEE Transactions on Knowledge and Data Engineering,2016,28(2):411-424. [25]ZHANG W,LI Y,ZHANG J,et al.A User Trajectory Identification Model with Fusion of Spatio-Temporal Behavior and Social Relation [J].Chinese Journal of Computers,2021(11):44. [26]KONG X,ZHANG J,YU P S.Inferring anchor links acrossmultiple heterogeneous social networks[C]//ACM Interna-tional Conference on Information & Knowledge Management.ACM,2013. [27]YANG Y,ZHAN D C,WU Y F,et al.Multi-network UserIdentification via Graph-Aware Embedding[C]//2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining.Springer,2018. [28]KOJIMA K,IKEDA K,TANI M.Short Paper:User Identification across Online Social Networks Based on Similarities among Distributions of Friends' Locations[C]//2019 IEEE Interna-tional Conference on Big Data(Big Data).IEEE,2019. [29]ZENG W,TANG R,WANG H,et al.User Identification Based on Integrating Multiple User Information across Online Social Networks [J].Security and Communication Networks,2021:1-14. [30]YING C X,CAI T L,LUO S J,et al.Do transformers really perform badly for graph representation?[C]//Advances in Neural Information Processing Systems 34.2021:28877-28888. [31]CHEN H,YIN H,SUN X,et al.Multi-level Graph Convolu-tional Networks for Cross-platform Anchor Link Prediction[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.ACM,2020:1503-1511. [32]LIU Q,LI Y,DUAN H,et al.Knowledge Graph ConstructionTechniques [J].Journal of Computer Research and Development,2016,53(3):582-600. [33]WU X,JIANG T,ZHU Y,et al.Knowledge graph for china's genealogy [J].IEEE Transactions on Knowledge and Data Engineering,2021,35(1):634-646. [34]SUN Z,WANG C,HU W,et al.Knowledge Graph AlignmentNetwork with Gated Multi-Hop Neighborhood Aggregation [J].Proceedings of the AAAI Conference on Artificial Intelligence,2020,34(1):222-229. [35]LI J,WANG Z,XIAO Z,et al.Large scale instance matching via multiple indexes and candidate selection [J].Knowledge-Based Systems,2013,50:112-120. [36]LU W,WU C.Literature Review on Entity Linking [J].Journal of the China Society for Scientific and Technical Information,2015,34(1):105-112. [37]ZHENG Y,WANG L H,ZHANG R C,et al.GeoLife:managing and understanding your past life over maps[C]//Proceedings of the 9th International Conference on Mobile Data Management.IEEE Press,2008:211-212. [38]SANG J T,LU D Y,XU C S.Overlapped User-based Cross-network Analysis:Exploring Variety in Big Social Media Data [J].Chinese Science Bulletin,2014,59(36):3554-3560. [39]TAO F,LIU W R,LIU J H,et al.Digital Twin and Its Potential Application Exploring [J].Computer Integrated Manufacturing Systems,2018,24(1):4-21. [40]LI L,MIN Z.The Strategy of Mining Association Rule Based on Cloud Computing[C]//2011 International Conference on Business Computing and Global Informatization(BCGIN).IEEE,2011. [41]SUN Y,KAMEL M S,WONG A,et al.Cost-sensitive boosting for classification of imbalanced data[J].Pattern Recognition,2007,40(12):3358-3378. [42]PING R,ZHOU Y S,LI D.Cost sensitive random forest classifi-cation algorithm for highly unbalanced data [J].Pattern Recognition and Artificial Intelligence 2020,33(3):249-257. [43]LI T,SRIKUMAR V.Augmenting neural networks with first-order logic[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.ACL,2019:102-113. [44]LV L Y.Link Prediction on Complex Networks [J].Journal of University of Electronic Science and Technology of China,2010,39(5):651-661. [45]LIN Y J,WU Y,LI H B.Internet Hirelings Semi-supervised Detection of Weibo Based on Affinity Propagation Algorithm [J].Netinfo Security,2022,22(3):85-96. |
|
||
首页