计算机科学 ›› 2019, Vol. 46 ›› Issue (9): 93-98.doi: 10.11896/j.issn.1002-137X.2019.09.012
张征, 王宏志, 丁小欧, 李建中, 高宏
ZHANG Zheng, WANG Hong-zhi, DING Xiao-ou, LI Jian-zhong, GAO Hong
摘要： 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。
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