一种基于深度学习的动态社交网络用户对齐方法

A Deep Learning Based Dynamic Social Network Alignment Method

社交网络对齐旨在从不同的社交网络中识别出属于同一自然人的社交账户.现有的相关研究大多着眼于静态社交网络的对齐上,然而,社交网络是动态发展的.本文观察到,这种动态性可以揭示出更多的决定性模式,从而更有利于社交网络的对齐,这种现象促使本文在动态场景中重新思考这个问题.于是,本文利用社交网络的动态性,设计一个深度学习架构来解决动态社交网络的对齐问题,其称为DeepDSA(Deep learning based Dynamic Social network Alignment method).首先设计一个深度序列模型来分别捕捉社交网络结构和属性的动态性;其次,对于每一个社交网络,通过保持相同用户结构和属性之间的相关性来融合二元动态,得到原始的综合嵌入表示;最后,以半监督的方式进行空间变换学习,并将每个网络的原始嵌入投影到一个目标子空间中,在该子空间中自然人是唯一表示的.本文在真实世界的数据集上进行大量的实验,证明DeepDSA方法相较于目前的主流算法提升了10%的对齐效果.

Social network alignment aims to identify social accounts belonging to the same natural person from different social networks.Most of the existing related researches focus on the alignment of static social networks.However,social networks are dynamically evolving.We observe that dynamics can reveal more discriminative patterns and thus can benefit social network alignment.This phenomenon motivates us to rethink this issue in dynamic scenarios.Therefore,we propose to leverage the dynamics of social networks and design a deep learning architecture to address the dynamic social network alignment problem,termed as DeepDSA.Specifically,we first design a deep sequence model to capture the dynamics of social network structure and attributes respectively.For each social network,we merged binary dynamics by maintaining the correlation between structure and attributes of the same user to obtain the original comprehensive embeddings.We finally perform spatial transformation learning in a semi-supervised manner,and project the original embedding of each network into a target subspace in which a natural person is uniquely represented.We conduct extensive experiments on realworld datasets and demonstrate the proposed DeepDSA achieves 10%improvement of precision against the current mainstream algorithm.