Collective classification via discriminative matrix factorization on sparsely labeled networks

Zhang, D; Yin, J; Zhu, X; Zhang, C

Collective classification via discriminative matrix factorization on sparsely labeled networks

Zhang, D

Yin, J Zhu, X Zhang, C

Permalink

Publication Type:: Conference Proceeding
Citation:: International Conference on Information and Knowledge Management, Proceedings, 2016, 24-28-October-2016 pp. 1563 - 1572
Issue Date:: 2016-10-24

Closed Access

	Filename	Description	Size
	p1563-zhang.pdf	Published version	1.73 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, D https://orcid.org/0000-0002-1803-5768	en_US
dc.contributor.author	Yin, J	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2016-10-24	en_US
dc.identifier.citation	International Conference on Information and Knowledge Management, Proceedings, 2016, 24-28-October-2016 pp. 1563 - 1572	en_US
dc.identifier.isbn	9781450340731	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121826
dc.description.abstract	© 2016 Copyright held by the owner/author(s). 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.	en_US
dc.relation.ispartof	International Conference on Information and Knowledge Management, Proceedings	en_US
dc.relation.isbasedon	10.1145/2983323.2983754	en_US
dc.title	Collective classification via discriminative matrix factorization on sparsely labeled networks	en_US
dc.type	Conference Proceeding
utslib.citation.volume	24-28-October-2016	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	24-28-October-2016	en_US

Abstract:

© 2016 Copyright held by the owner/author(s). 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/121826