NOVA: A novel and efficient framework for finding subgraph isomorphism mappings in large graphs

Zhu, K; Zhang, Y; Lin, X; Zhu, G; Wang, W

NOVA: A novel and efficient framework for finding subgraph isomorphism mappings in large graphs

Zhu, K Zhang, Y

Lin, X Zhu, G Wang, W

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 5981 LNCS (PART 1), pp. 140 - 154
Issue Date:: 2010-12-28

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Field	Value	Language
dc.contributor.author	Zhu, K	en_US
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638	en_US
dc.contributor.author	Lin, X	en_US
dc.contributor.author	Zhu, G	en_US
dc.contributor.author	Wang, W	en_US
dc.date.issued	2010-12-28	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 5981 LNCS (PART 1), pp. 140 - 154	en_US
dc.identifier.isbn	3642120253	en_US
dc.identifier.isbn	9783642120251	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28960
dc.description.abstract	Considerable efforts have been spent in studying subgraph problem. Traditional subgraph containment query is to retrieve all database graphs which contain the query graph g. A variation to that is to find all occurrences of a particular pattern(the query) in a large database graph. We call it subgraph matching problem. The state of art solution to this problem is GADDI. In this paper, we will propose a more efficient index and algorithm to answer subgraph matching problem. The index is based on the label distribution of neighbourhood vertices and it is structured as a multi-dimensional vector signature. A novel algorithm is also proposed to further speed up the isomorphic enumeration process. This algorithm attempts to maximize the computational sharing. It also attempts to predict some enumeration state is impossible to lead to a final answer by eagerly pruning strategy. We have performed extensive experiments to demonstrate the efficiency and the effectiveness of our technique. © Springer-Verlag Berlin Heidelberg 2010.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-12026-8_13	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	NOVA: A novel and efficient framework for finding subgraph isomorphism mappings in large graphs	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 1	en_US
utslib.citation.volume	5981 LNCS	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Tsukuba, Japan	en_US
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	PART 1	en_US
pubs.publication-status	Published	en_US
pubs.volume	5981 LNCS	en_US

Abstract:

Considerable efforts have been spent in studying subgraph problem. Traditional subgraph containment query is to retrieve all database graphs which contain the query graph g. A variation to that is to find all occurrences of a particular pattern(the query) in a large database graph. We call it subgraph matching problem. The state of art solution to this problem is GADDI. In this paper, we will propose a more efficient index and algorithm to answer subgraph matching problem. The index is based on the label distribution of neighbourhood vertices and it is structured as a multi-dimensional vector signature. A novel algorithm is also proposed to further speed up the isomorphic enumeration process. This algorithm attempts to maximize the computational sharing. It also attempts to predict some enumeration state is impossible to lead to a final answer by eagerly pruning strategy. We have performed extensive experiments to demonstrate the efficiency and the effectiveness of our technique. © Springer-Verlag Berlin Heidelberg 2010.

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http://hdl.handle.net/10453/28960