Multiplex network infomax:Multiplex network embedding via information fusion  

作　　者：Qiang Wang Hao Jiang Ying Jiang Shuwen Yi Qi Nie Geng Zhang 

机构地区：[1]Electronic Information School,Wuhan University,Wuhan,430072,China [2]School of Computer Science and Engineering,Sun Yat-sen University,Guangzhou,510006,China [3]China Electric Power Research Institute,Beijing,100192,China

出　　处：《Digital Communications and Networks》2023年第5期1157-1168,共12页数字通信与网络（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(NSFC)under Grant U19B2004;in part by National Key R&D Program of China under Grant 2022YFB2901202;in part by the Open Funding Projects of the State Key Laboratory of Communication Content Cognition(No.20K05 and No.A02107);in part by the Special Fund for Science and Technology of Guangdong Province under Grant 2019SDR002.

摘　　要：For networking of big data applications,an essential issue is how to represent networks in vector space for further mining and analysis tasks,e.g.,node classification,clustering,link prediction,and visualization.Most existing studies on this subject mainly concentrate on monoplex networks considering a single type of relation among nodes.However,numerous real-world networks are naturally composed of multiple layers with different relation types;such a network is called a multiplex network.The majority of existing multiplex network embedding methods either overlook node attributes,resort to node labels for training,or underutilize underlying information shared across multiple layers.In this paper,we propose Multiplex Network Infomax(MNI),an unsupervised embedding framework to represent information of multiple layers into a unified embedding space.To be more specific,we aim to maximize the mutual information between the unified embedding and node embeddings of each layer.On the basis of this framework,we present an unsupervised network embedding method for attributed multiplex networks.Experimental results show that our method achieves competitive performance on not only node-related tasks,such as node classification,clustering,and similarity search,but also a typical edge-related task,i.e.,link prediction,at times even outperforming relevant supervised methods,despite that MNI is fully unsupervised.

关 键 词：Network embedding Multiplex network Mutual information maximization 

分 类 号：TN91[电子电信—通信与信息系统]