Large-scale supervised similarity learning in networks. Issue 3 (September 2016)

• Record Type: Journal Article
• Authors: Chang, Shiyu; Qi, Guo-Jun; Yang, Yingzhen; Aggarwal, Charu; Zhou, Jiayu; Wang, Meng; Huang, Thomas
• Is Part Of: Knowledge and information systems. Volume 48:Issue 3(2016:Sep.)
• Page Start: 707
• Page End: 740
• Publication Date: 2016-09
• Subjects: Supervised network similarity learning -- Supervised network embedding -- Large-scale network -- Supervised matrix factorization -- Link content consistency; Expert systems (Computer science) -- Periodicals; Information storage and retrieval systems -- Periodicals; 006.33
• DOI: 10.1007/s10115-015-0894-8
• Languages: English
• ISSNs: 0219-1377

Abstract

Abstract The problem of similarity learning is relevant to many data mining applications, such as recommender systems, classification, and retrieval. This problem is particularly challenging in the context of networks, which contain different aspects such as the topological structure, content, and user supervision. These different aspects need to be combined effectively, in order to create a holistic similarity function. In particular, while most similarity learning methods in networks such asSimRank utilize the topological structure, the user supervision and content are rarely considered. In this paper, a factorized similarity learning (FSL) is proposed to integrate the link, node content, and user supervision into a uniform framework. This is learned by using matrix factorization, and the final similarities are approximated by the span of low-rank matrices. The proposed framework is further extended to a noise-tolerant version by adopting a hinge loss alternatively. To facilitate efficient computation on large-scale data, a parallel extension is developed. Experiments are conducted on theDBLP andCoRA data sets. The results show thatFSL is robust and efficient and outperforms the state of the art. The code for the learning algorithm used in our experiments is available athttp://www.ifp.illinois.edu/~chang87/ .