Algorithms for approximate graph matching
Labeled graphs are graphs in which each node and edge has a label. The distance between two labeled graphs is considered to be the weighted sum of the costs of edit operations (insert, delete, and relabel the nodes and edges) to transform one graph to the other. The paper considers two variants of t...
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| Veröffentlicht in: | Information sciences 1995, Vol.82 (1), p.45-74 |
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| container_title | Information sciences |
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| creator | Wang, Jason T.L. Zhang, Kaizhong Chirn, Gung-Wei |
| description | Labeled graphs are graphs in which each node and edge has a label. The distance between two labeled graphs is considered to be the weighted sum of the costs of edit operations (insert, delete, and relabel the nodes and edges) to transform one graph to the other. The paper considers two variants of the approximate graph matching problem (AGM): Given a pattern graph
P and a data graph
D:
1.
1. What is the distance between
P and
D?
2.
2. What is the minimum distance between
P and
D when subgraphs can be freely removed from
D?
We first show that no efficient algorithm can solve either variant of the AGM unless P = NP. Then we present several heuristic algorithms leading to approximate solutions. The heuristics are based on probabilistic hill climbing and maximum flow techniques. Our experimental results involving the comparison of real and simulated data demonstrate the good performance of the algorithms presented. |
| doi_str_mv | 10.1016/0020-0255(94)00057-I |
| format | Article |
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P and a data graph
D:
1.
1. What is the distance between
P and
D?
2.
2. What is the minimum distance between
P and
D when subgraphs can be freely removed from
D?
We first show that no efficient algorithm can solve either variant of the AGM unless P = NP. Then we present several heuristic algorithms leading to approximate solutions. The heuristics are based on probabilistic hill climbing and maximum flow techniques. Our experimental results involving the comparison of real and simulated data demonstrate the good performance of the algorithms presented.</description><identifier>ISSN: 0020-0255</identifier><identifier>EISSN: 1872-6291</identifier><identifier>DOI: 10.1016/0020-0255(94)00057-I</identifier><identifier>CODEN: ISIJBC</identifier><language>eng</language><publisher>New York, NY: Elsevier Inc</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Exact sciences and technology ; Information retrieval. Graph ; Pattern recognition. Digital image processing. Computational geometry ; Theoretical computing</subject><ispartof>Information sciences, 1995, Vol.82 (1), p.45-74</ispartof><rights>1995</rights><rights>1995 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c364t-b3020a96b55f4ca59a134770454d2e777d0388383e867d62aeac59434477e5143</citedby><cites>FETCH-LOGICAL-c364t-b3020a96b55f4ca59a134770454d2e777d0388383e867d62aeac59434477e5143</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/0020-0255(94)00057-I$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,4024,27923,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=3585579$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Jason T.L.</creatorcontrib><creatorcontrib>Zhang, Kaizhong</creatorcontrib><creatorcontrib>Chirn, Gung-Wei</creatorcontrib><title>Algorithms for approximate graph matching</title><title>Information sciences</title><description>Labeled graphs are graphs in which each node and edge has a label. The distance between two labeled graphs is considered to be the weighted sum of the costs of edit operations (insert, delete, and relabel the nodes and edges) to transform one graph to the other. The paper considers two variants of the approximate graph matching problem (AGM): Given a pattern graph
P and a data graph
D:
1.
1. What is the distance between
P and
D?
2.
2. What is the minimum distance between
P and
D when subgraphs can be freely removed from
D?
We first show that no efficient algorithm can solve either variant of the AGM unless P = NP. Then we present several heuristic algorithms leading to approximate solutions. The heuristics are based on probabilistic hill climbing and maximum flow techniques. Our experimental results involving the comparison of real and simulated data demonstrate the good performance of the algorithms presented.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Information retrieval. Graph</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Theoretical computing</subject><issn>0020-0255</issn><issn>1872-6291</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1995</creationdate><recordtype>article</recordtype><recordid>eNp9kEtPwzAQhC0EEuXxDzjkgBA9BNbxK74gVRWPSpW4wNlynU1rlCbBThH8e1xa9chp9_DN7M4QckXhjgKV9wAF5FAIcav5GACEymdHZERLVeSy0PSYjA7IKTmL8SNBXEk5IuNJs-yCH1brmNVdyGzfh-7br-2A2TLYfpWl1a18u7wgJ7VtIl7u5zl5f3p8m77k89fn2XQyzx2TfMgXLF2yWi6EqLmzQlvKuFLABa8KVEpVwMqSlQxLqSpZWLROaM54glBQzs7Jzc43PfK5wTiYtY8Om8a22G2iKZhUHIAmkO9AF7oYA9amD-nx8GMomG0vZhvabEMbzc1fL2aWZNd7fxudbepgW-fjQctEKYTSCXvYYZiyfnkMJjqPrcPKB3SDqTr__51fy7Vz8Q</recordid><startdate>1995</startdate><enddate>1995</enddate><creator>Wang, Jason T.L.</creator><creator>Zhang, Kaizhong</creator><creator>Chirn, Gung-Wei</creator><general>Elsevier Inc</general><general>Elsevier Science</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>1995</creationdate><title>Algorithms for approximate graph matching</title><author>Wang, Jason T.L. ; Zhang, Kaizhong ; Chirn, Gung-Wei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c364t-b3020a96b55f4ca59a134770454d2e777d0388383e867d62aeac59434477e5143</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Information retrieval. Graph</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Jason T.L.</creatorcontrib><creatorcontrib>Zhang, Kaizhong</creatorcontrib><creatorcontrib>Chirn, Gung-Wei</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Information sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Jason T.L.</au><au>Zhang, Kaizhong</au><au>Chirn, Gung-Wei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Algorithms for approximate graph matching</atitle><jtitle>Information sciences</jtitle><date>1995</date><risdate>1995</risdate><volume>82</volume><issue>1</issue><spage>45</spage><epage>74</epage><pages>45-74</pages><issn>0020-0255</issn><eissn>1872-6291</eissn><coden>ISIJBC</coden><abstract>Labeled graphs are graphs in which each node and edge has a label. The distance between two labeled graphs is considered to be the weighted sum of the costs of edit operations (insert, delete, and relabel the nodes and edges) to transform one graph to the other. The paper considers two variants of the approximate graph matching problem (AGM): Given a pattern graph
P and a data graph
D:
1.
1. What is the distance between
P and
D?
2.
2. What is the minimum distance between
P and
D when subgraphs can be freely removed from
D?
We first show that no efficient algorithm can solve either variant of the AGM unless P = NP. Then we present several heuristic algorithms leading to approximate solutions. The heuristics are based on probabilistic hill climbing and maximum flow techniques. Our experimental results involving the comparison of real and simulated data demonstrate the good performance of the algorithms presented.</abstract><cop>New York, NY</cop><pub>Elsevier Inc</pub><doi>10.1016/0020-0255(94)00057-I</doi><tpages>30</tpages></addata></record> |
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| ispartof | Information sciences, 1995, Vol.82 (1), p.45-74 |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Information retrieval. Graph Pattern recognition. Digital image processing. Computational geometry Theoretical computing |
| title | Algorithms for approximate graph matching |
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