Diffusion network embedding. (April 2019)

• Record Type: Journal Article
• Authors: Shi, Yong; Lei, Minglong; Yang, Hong; Niu, Lingfeng
• Is Part Of: Pattern recognition. Volume 88(2019:Apr.)
• Page Start: 518
• Page End: 531
• Publication Date: 2019-04
• Subjects: Network embedding -- Cascades -- Diffusion process -- Network inference -- Dimension reduction; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.12.004
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: The network diffusion based embedding method solves the limitations of random walks. Diffusion driven process is employed to capture both depth and breadth information. The time dimension attached to node sequences strengthens information preserving. The network inference technique based on cascades captures the global information. Our method is more robust to low sampling frequency and highly unbalanced networks. Abstract: In network embedding, random walks play a fundamental role in preserving network structures. However, random walk methods have two limitations. First, they are unstable when either the sampling frequency or the number of node sequences changes. Second, in highly biased networks, random walks are likely to bias to high-degree nodes and neglect the global structure information. To solve the limitations, we present in this paper a network diffusion embedding method. To solve the first limitation, our method uses a diffusion driven process to capture both depth and breadth information in networks. Temporal information is also included into node sequences to strengthen information preserving. To solve the second limitation, our method uses the network inference method based on information diffusion cascades to capture the global network information. Experiments show that the new proposed method is more robust to highly unbalanced networks and well performed when sampling under each node is rare.