Clustering with multiple graphs

  • Tang W
  • Lu Z
  • Dhillon I
  • 131


    Mendeley users who have this article in their library.
  • 109


    Citations of this article.


In graph-based learning models, entities are often represented as vertices in an undirected graph with weighted edges describing the relationships between entities. In many real-world applications, however, entities are often associated with relations of different types and/or from different sources, which can be well captured by multiple undirected graphs over the same set of vertices. How to exploit such multiple sources of information to make better inferences on entities remains an interesting open problem. In this paper, we focus on the problem of clustering the vertices based on multiple graphs in both unsupervised and semi-supervised settings. As one of our contributions, we propose Linked Matrix Factorization (LMF) as a novel way of fusing information from multiple graph sources. In LMF, each graph is approximated by matrix factorization with a graph-specific factor and a factor common to all graphs, where the common factor provides features for all vertices. Experiments on SIAM journal data show that (1) we can improve the clustering accuracy through fusing multiple sources of information with several models, and (2) LMF yields superior or competitive results compared to other graph-based clustering methods.

Author-supplied keywords

  • Clustering
  • Graph
  • Multiple sources
  • Semisupervised learning

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Wei Tang

  • Zhengdong Lu

  • Inderjit S. Dhillon

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free