Collective classification via discriminative matrix factorization on sparsely labeled networks

Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

32 Citations (Scopus)


We address the problem of classifying sparsely labeled networks, where labeled nodes in the network are extremely scarce. Existing algorithms, such as collective classification, have been shown to be effective for jointly deriving labels of related nodes, by exploiting label dependencies among neighboring nodes. However, when the network is sparsely labeled, most nodes have too few or even no connections to labeled nodes. This makes it very difficult to leverage supervised knowledge from labeled nodes to accurately estimate label dependencies, thereby largely degrading classification accuracy. In this paper, we propose a novel discriminative matrix factorization (DMF) based algorithm that effectively learns a latent network representation by exploiting topo-logical paths between labeled and unlabeled nodes, in addition to nodes' content information. The main idea is to use matrix factorization to obtain a compact representation of the network that fully encodes nodes' content information and network structure, and unleash discriminative power inferred from labeled nodes to directly benefit collective classification. We formulate a new matrix factorization objective function that integrates network representation learning with an empirical loss minimization for classifying node labels. An efficient optimization algorithm based on conjugate gradient methods is proposed to solve the new objective function. Experimental results on real-world networks show that DMF yields superior performance gain over the state-of-the-art baselines on sparsely labeled networks.

Original languageEnglish
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9781450340731
Publication statusPublished - 24 Oct 2016
Externally publishedYes
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: 24 Oct 201628 Oct 2016

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/TerritoryUnited States


  • Collective classification
  • Matrix factorization
  • Network representation learning
  • Sparsely labeled networks

ASJC Scopus subject areas

  • General Decision Sciences
  • General Business,Management and Accounting


Dive into the research topics of 'Collective classification via discriminative matrix factorization on sparsely labeled networks'. Together they form a unique fingerprint.

Cite this