Multiplex network infomax: Multiplex network embedding via information fusion

• Published in: Digital communications and networks Vol. 9; no. 5; pp. 1157 - 1168
• Main Authors: Wang, Qiang, Jiang, Hao, Jiang, Ying, Yi, Shuwen, Nie, Qi, Zhang, Geng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2023; Electronic Information School,Wuhan University,Wuhan,430072,China%School of Computer Science and Engineering,Sun Yat-sen University,Guangzhou,510006,China%Electronic Information School,Wuhan University,Wuhan,430072,China; China Electric Power Research Institute,Beijing,100192,China; KeAi Communications Co., Ltd
• Subjects: Multiplex network; Mutual information maximization; Network embedding; Mutual information maximization; Network embedding; Multiplex network
• ISSN: 2352-8648; 2468-5925; 2352-8648
• DOI: 10.1016/j.dcan.2022.10.002

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.