Abstract: With rapid development and popularization of Internet technologies,more and more users have begun to share and exchange various information through social networks.The same user in the network may apply for multiple diffe-rent accounts to distribute information,and these accounts constitute the associated users in the network.Effectively mining associated users in social networks can suppress false information and illegal behaviors in the network,and thus ensure the security and fairness of the network environment.Existing associated user mining methods only consider user attributes or user relationship information without merging multiple types of information contained in the network comprehensively.In addition,most methods draw lessons from the methods in other fields,such as de-anonymization,and they can’t accurately solve the problem of associated user mining.In light of this,this paper proposed an associated user mining algorithm based on multi-information fusion representation learning(AUMA-MRL).In this algorithm,the idea of network representation learning is utilized to learn various dimensional information in the networks,such as user attributes,network topology,etc.Then the learned multi-information is effectively fused to obtain multi-information node embedding,which can accurately characterize multiple types of information in networks,and mine associated users in networks through similarity vectors between node embedding.The proposed algorithm was validated on three real networks namely protein network PPI and social network Flickr,Facebook.In the experiment,the accuracy and recall rate is selected as the performance evaluation indexes.The results show that the recall rate of proposed algorithm is increased by 17.5% on average compared with the existing classical algorithms,and it can effectively mine associated users in networks.

Key words: Associated users, Node embedding, Representation learning, Social networks security