High efficiency and quality: Large graphs matching

Zhu, Y; Qin, L; Yu, JX; Ke, Y; Lin, X

High efficiency and quality: Large graphs matching

Zhu, Y Qin, L

Yu, JX

Ke, Y Lin, X

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Publication Type:: Conference Proceeding
Citation:: International Conference on Information and Knowledge Management, Proceedings, 2011, pp. 1755 - 1764
Issue Date:: 2011-12-13

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Field	Value	Language
dc.contributor.author	Zhu, Y	en_US
dc.contributor.author	Qin, L https://orcid.org/0000-0001-6068-5062	en_US
dc.contributor.author	Yu, JX https://orcid.org/0000-0002-9738-827X	en_US
dc.contributor.author	Ke, Y	en_US
dc.contributor.author	Lin, X	en_US
dc.date.issued	2011-12-13	en_US
dc.identifier.citation	International Conference on Information and Knowledge Management, Proceedings, 2011, pp. 1755 - 1764	en_US
dc.identifier.isbn	9781450307178	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32035
dc.description.abstract	Graph matching plays an essential role in many real applications. In this paper, we study how to match two large graphs by maximizing the number of matched edges, which is known as maximum common subgraph matching and is NP-hard. To find exact matching, it cannot handle a graph with more than 30 nodes. To find an approximate matching, the quality can be very poor. We propose a novel two-step approach which can efficiently match two large graphs over thousands of nodes with high matching quality. In the first step, we propose an anchor-selection/expansion approach to compute a good initial matching. In the second step, we propose a new approach to refine the initial matching. We give the optimality of our refinement and discuss how to randomly refine the matching with different combinations. We conducted extensive testing using real and synthetic datasets, and will report our findings. © 2011 ACM.	en_US
dc.relation.ispartof	International Conference on Information and Knowledge Management, Proceedings	en_US
dc.relation.isbasedon	10.1145/2063576.2063831	en_US
dc.title	High efficiency and quality: Large graphs matching	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Glasgow, United Kingdom
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Graph matching plays an essential role in many real applications. In this paper, we study how to match two large graphs by maximizing the number of matched edges, which is known as maximum common subgraph matching and is NP-hard. To find exact matching, it cannot handle a graph with more than 30 nodes. To find an approximate matching, the quality can be very poor. We propose a novel two-step approach which can efficiently match two large graphs over thousands of nodes with high matching quality. In the first step, we propose an anchor-selection/expansion approach to compute a good initial matching. In the second step, we propose a new approach to refine the initial matching. We give the optimality of our refinement and discuss how to randomly refine the matching with different combinations. We conducted extensive testing using real and synthetic datasets, and will report our findings. © 2011 ACM.

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http://hdl.handle.net/10453/32035