社交网络中同一用户的识别

Identification of Same User in Social Networks

对不同社交全局网络中同一用户的身份识别进行了相关研究,将社交网络建模为节点带有属性值且含有一个中心节点的网络,即ego-network,并就社交网络中身份识别的问题设计了相关算法。为挖掘同一个用户的节点对,对用户的属性、好友关系的相似度进行了建模,从而综合评价了不同社交网络中节点间的相似度,即为用户匹配评分,将其作为节点匹配的优先度;然后通过改进后的RCM算法得到全局最优的匹配结果;最后剪掉用户匹配评分较低的已匹配用户对以达到更好的效果。基于真实数据集,实验对比了该算法与几种相关算法的表现,并分析了不同参数对实验效果的影响,验证了所提算法的合理性。

This paper carried on the related research of the same user identification in different social global networks.The social network was modeled as a network with attribute value and a central node,namely ego-network.And aiming at the identification problem in the social network,this paper designed related algorithm.In order to mine the node pairs of the same user,the user’s attributes and the similarity of the friends’ relationship are modeled,so as to comprehensively evaluate the similarities among the nodes in different social networks,namely,to get the user match score and to use it in node matching.Then through the improved RCM algorithm,the global optimal matching results are obtained, and finally the matching user pairs with lower user match scores are cut off to achieve better results.Based on real datasets,the performance of the algorithm is compared with several related algorithms.The effect of different parameters on experimental results is also analyzed and the rationality of the proposed algorithm is verified.