Large-scale supervised similarity learning in networks

Shiyu Chang, Guo Jun Qi, Yingzhen Yang, Charu C. Aggarwal, Jiayu Zhou, Meng Wang, Thomas S. Huang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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

Original languageEnglish (US)
Pages (from-to)707-740
Number of pages34
JournalKnowledge and Information Systems
Issue number3
StatePublished - Sep 1 2016
Externally publishedYes


  • Large-scale network
  • Link content consistency
  • Supervised matrix factorization
  • Supervised network embedding
  • Supervised network similarity learning

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence


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