Large-Scale Supervised Similarity Learning In Networks
Keywords
Large-scale network; Link content consistency; Supervised matrix factorization; Supervised network embedding; Supervised network similarity learning
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 as SimRank 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 the DBLP and CoRA data sets. The results show that FSL is robust and efficient and outperforms the state of the art. The code for the learning algorithm used in our experiments is available at http://www.ifp.illinois.edu/~chang87/.
Publication Date
9-1-2016
Publication Title
Knowledge and Information Systems
Volume
48
Issue
3
Number of Pages
707-740
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1007/s10115-015-0894-8
Copyright Status
Unknown
Socpus ID
84944916452 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84944916452
STARS Citation
Chang, Shiyu; Qi, Guo Jun; Yang, Yingzhen; Aggarwal, Charu C.; and Zhou, Jiayu, "Large-Scale Supervised Similarity Learning In Networks" (2016). Scopus Export 2015-2019. 3524.
https://stars.library.ucf.edu/scopus2015/3524