Signed network representation with novel node proximity evaluation. (April 2022)

• Record Type: Journal Article
• Authors: Xu, Pinghua; Hu, Wenbin; Wu, Jia; Liu, Weiwei
• Is Part Of: Neural networks. Volume 148(2022)
• Page Start: 142
• Page End: 154
• Publication Date: 2022-04
• Subjects: Signed social network -- Network representation -- Node proximity; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2022.01.014
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Currently, signed network representation has been applied to many fields, e.g., recommendation platforms. A mainstream paradigm of network representation is to map nodes onto a low-dimensional space, such that the node proximity of interest can be preserved. Thus, a key aspect is the node proximity evaluation. Accordingly, three new node proximity metrics were proposed in this study, based on the rigorous theoretical investigation on a new distance metric - signed average first-passage time (SAFT). SAFT derives from a basic random-walk quantity for unsigned networks and can capture high-order network structure and edge signs. We conducted network representation using the proposed proximity metrics and empirically exhibited our advantage in solving two downstream tasks — sign prediction and link prediction. The code is publicly available.