计算机科学 ›› 2019, Vol. 46 ›› Issue (4): 77-82.doi: 10.11896/j.issn.1002-137X.2019.04.012
韩忠明1,2, 郑晨烨1, 段大高1, 董健3
HAN Zhong-ming1,2, ZHENG Chen-ye1, DUAN Da-gao1, DONG Jian3
摘要: 随着互联网技术的迅速发展和普及,越来越多的用户开始通过社会网络进行各种信息的分享与交流。网络中同一用户可能申请多个不同账号进行信息发布,这些账号构成了网络中的关联用户。准确、有效地挖掘社会网络中的关联用户能够抑制网络中的虚假信息和不法行为,从而保证网络环境的安全性和公平性。现有的关联用户挖掘方法仅考虑了用户属性或用户关系信息,未对网络中含有的多类信息进行有效融合以及综合考虑。此外,大多数方法借鉴其他领域的方法进行研究,如去匿名化问题,这些方法不能准确解决关联用户挖掘问题。为此,文中针对网络关联用户挖掘问题,提出了基于多信息融合表示学习的关联用户挖掘算法(Associated Users Mining Algorithm based on Multi-information fusion Representation Learning,AUMA-MRL)。该算法使用网络表示学习的思想对网络中多种不同维度的信息(如用户属性、网络拓扑结构等)进行学习,并将学习得到的表示进行有效融合,从而得到多信息融合的节点嵌入。这些嵌入可以准确表征网络中的多类信息,基于习得的节点嵌入构造相似性向量,从而对网络中的关联用户进行挖掘。文中基于3个真实网络数据对所提算法进行验证,实验网络数据包括蛋白质网络PPI以及社交网络Flickr和Facebook,使用关联用户挖掘结果的精度和召回率作为性能评价指标对所提算法进行有效性验证。结果表明,与现有经典算法相比,所提算法的召回率平均提高了17.5%,能够对网络中的关联用户进行有效挖掘。
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[1]ZHOU X P,LIANG X,ZHAO J C,et al.A Survey of Related User Mining Methods for Social Network[J].Journal of Software,2017,28(6):1565-1583.(in Chinese) 周小平,梁循,赵吉超,等.面向社会网络融合的关联用户挖掘方法综述[J].软件学报,2017,28(6):1565-1583. [2]CAI J,STRUBE M.End-to-end coreference resolution via hy- pergraph partitioning[C]∥Proceedings of the 23rd InternationalConference on Computational Linguistics.Association for Computational Linguistics,2010:143-151. [3]WANG J,LI G,YU J X,et al.Entity matching:How similar is similar[J] Proceedings of the VLDB Endowment,2011,4(10):622-633. [4]KALASHNIKOVD V,CHEN Z Q,MEHROTRA S,et al.Web People Search via Connection Analysis[J].IEEE Transactions on Knowledge and Data Engineering,2008,20(11):1550-1565. [5]QIAN Y,HU Y,CUI J,et al.Combining machine learning and human judgment in author disambiguation[C]∥Proceedings of the 20th ACM International Conference on Information and Knowledge Management.ACM,2011:1241-1246. [6]TANG J,FONG A C M,WANG B,et al.A Unified Probabilistic Framework for Name Disambiguation in Digital Library[J].IEEE Transactions on Knowledge and Data Engineering,2012,24(6):975-987. [7]LIU J,ZHANG F,SONG X,et al.What’s in a name? an unsupervised approach to link users across communities[C]∥ACM International Conference on Web Search and Data Mining.ACM,2013:495-504. [8]ZAFARANI R,LIU H.Connecting users across social media sites:a behavioral-modeling approach[C]∥ACM SIGKDD International Conference on Knowledge Discovery and Data Mi-ning.ACM,2013:41-49. [9]ZHANG H,KAN M Y,LIU Y,et al.Online Social Network Profile Linkage[M]∥Information Retrieval Technology.Springer International Publishing,2014:197-208. [10]NARAYANAN A,SHMATIKOV V.De-anonymizing Social Networks[C]∥Security and Privacy IEEE Symposium.IEEE,2009:173-187. [11]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,2015,28(2):411-424. [12]FU H,ZHANG A,XIE X.Effective social graph deanonymization based on graph structure and descriptive information[C]∥ACM Transactions on Intelligent Systems and Technology (TIST),2015,6(4):1-29. [13]SINGH R,XU J B,BERGER B.Global alignment of multiple protein interaction networks with application to functional orthology detection[J].Proceedings of the National Academy of Sciences of the United States of America,2008,105(35):12763-12768. [14]CAI H Y,ZHENG V W,CHANG K.A Comprehensive Survey of Graph Embedding:Problems,Techniques and Applications[J].IEEE Transactions on Knowledge and Data Engineering,2018,30(9):1616-1637. [15]WANG D,CUI P,ZHU W.Structural Deep Network Embed- ding[C]∥Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2016:1225-1234. [16]KIPF T N,WELLING M.Semi-Supervised Classification with Graph Convolutional Networks[J].arXiv preprint arXiv:1609.02907,2016. [17]HAMILTON W,YING Z,LESKOVEC J.Inductive representation learning on large graphs[M]∥Advances in Neural Information Processing Systems.Bertin:Springer,2017:1024-1034. [18]ANDREW G,ARORA R,BILMES J,et al.Deep canonical correlation analysis[C]∥International Conference on Machine Learning.JMLR.org,2013:1247-1255. [19]BURGES C J C.A Tutorial on Support Vector Machines for Pattern Recognition[J].Data Mining and Knowledge Discovery,1998,2(2):121-167. |
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