面向社会网络融合的关联用户挖掘方法综述

Correlating User Mining Methods for Social Network Integration: A Survey

现阶段大多数社会网络的研究都集中于单一的社会网络内部.社会网络融合为社会计算等各项研究提供更充分的用户行为数据和更完整的网络结构,从而更有利于人们通过社会网络理解和挖掘人类社会,具有重要的理论价值和实践意义.准确、全面、快速地关联用户挖掘,是大型社会网络融合的根本问题.社会网络中的关联用户挖掘旨在通过挖掘不同社会网络中同属于同一自然人的不同账号,从而实现社会网络的深度融合,近年来已引起人们的广泛关注.然而,社会网络的自身数据量大、用户属性相似、稀疏且存在虚假和不一致等特点,给关联用户挖掘带来了极大的挑战.分析了面向社会网络融合的关联用户挖掘所存在的困难,从用户属性、用户关系及其综合这3个方面梳理了当前关联用户挖掘的研究现状.最后,总结并展望了关联用户挖掘的研究方向.

Social network （SN） has become a popular research field in both academia and industry. However, most of the current studies in this field mainly focuses on a single SN. Obviously, the integration of SNs, termed as social network integration （SNI）, provides more sufficient user behavior data and more complete network structure for the studies on SN such as social computing. Additionally, SNI is more effective in excavating and understanding human society through SNs. Thus, it has significant theoretical and practical value to explore problems in SNI. Correlating users refer to the user accounts belonging to the same individual in different SNs. Since users naturally bridge the SNs, correlating user mining problem is the fundamental task of SNI, hence having attracted extensive attention. Due to the unfavorable characteristics of SN, correlating user mining problem is still a hard nut to crack. In this paper, the difficulties in the correlating user mining task are analyzed, and the methods addressing this issue are summarized. Finally, some potential future research work is suggested.