基于融合表示学习的跨社交网络用户身份匹配

Cross-social Network User Identity Matching Based on Fusion Representation Learning

针对现有跨社交网络用户身份匹配算法准确率较低与数据难以获取等问题,提出一种新的跨社交网络用户身份匹配算法。利用已知匹配的账号节点,通过网络融合算法使跨网络问题转化为单一网络问题,对用户名信息进行向量化表示,并与拓扑结构信息向量融合,运用网络表示学习技术,得到融合用户名和拓扑结构2种信息的账号节点向量,实现用户身份匹配。实验结果表明,该算法的平均F1值达到79.7%,比传统的机器学习算法及现有2种基准算法高7.3%~28.8%,有效提升了用户身份匹配的效果。

Aiming at the problems that the existing cross-social network user identity matching algorithm has low accuracy and difficult data acquisition,a new cross-social network user identity matching algorithm is proposed.Using the known matching account nodes,the network fusion algorithm is used to transform the cross-network problem into a single network problem,and the user name information is vectorized and integrated with the topology information vector,and the network representation learning technology is used to obtain the fusion user name and topology.The account node vector of the two types of information is structured to implement user identity matching.Experimental results show that the average F1 value of the algorithm is 79.7%,which is 7.3%~28.8%higher than the traditional machine learning algorithm and the existing two benchmark algorithms,it effectively improves the user identity matching effect.