Energy-Based Clustering of Graphs with Nonuniform Degrees
Widely varying node degrees occur in software dependency graphs, hyperlink structures, social networks, and many other real-world graphs. Finding dense subgraphs in such graphs is of great practical interest, as these clusters may correspond to cohesive software modules, semantically related documen...
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| creator | Noack, Andreas |
| description | Widely varying node degrees occur in software dependency graphs, hyperlink structures, social networks, and many other real-world graphs. Finding dense subgraphs in such graphs is of great practical interest, as these clusters may correspond to cohesive software modules, semantically related documents, and groups of friends or collaborators. Many existing clustering criteria and energy models are biased towards clustering together nodes with high degrees. In this paper, we introduce a clustering criterion based on normalizing cuts with edge numbers (instead of node numbers), and a corresponding energy model based on edge repulsion (instead of node repulsion) that reveal clusters without this bias. |
| doi_str_mv | 10.1007/11618058_28 |
| format | Book Chapter |
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Finding dense subgraphs in such graphs is of great practical interest, as these clusters may correspond to cohesive software modules, semantically related documents, and groups of friends or collaborators. Many existing clustering criteria and energy models are biased towards clustering together nodes with high degrees. In this paper, we introduce a clustering criterion based on normalizing cuts with edge numbers (instead of node numbers), and a corresponding energy model based on edge repulsion (instead of node repulsion) that reveal clusters without this bias.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540314257</identifier><identifier>ISBN: 3540314253</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540316671</identifier><identifier>EISBN: 9783540316671</identifier><identifier>DOI: 10.1007/11618058_28</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Cluster Criterion ; Computer science; control theory; systems ; Energy Model ; Exact sciences and technology ; Graph Cluster ; Information retrieval. Graph ; Information systems. Data bases ; Large Graph ; Memory organisation. 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Finding dense subgraphs in such graphs is of great practical interest, as these clusters may correspond to cohesive software modules, semantically related documents, and groups of friends or collaborators. Many existing clustering criteria and energy models are biased towards clustering together nodes with high degrees. In this paper, we introduce a clustering criterion based on normalizing cuts with edge numbers (instead of node numbers), and a corresponding energy model based on edge repulsion (instead of node repulsion) that reveal clusters without this bias.</description><subject>Applied sciences</subject><subject>Cluster Criterion</subject><subject>Computer science; control theory; systems</subject><subject>Energy Model</subject><subject>Exact sciences and technology</subject><subject>Graph Cluster</subject><subject>Information retrieval. Graph</subject><subject>Information systems. Data bases</subject><subject>Large Graph</subject><subject>Memory organisation. Data processing</subject><subject>Node Versus</subject><subject>Software</subject><subject>Theoretical computing</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540314257</isbn><isbn>3540314253</isbn><isbn>3540316671</isbn><isbn>9783540316671</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2006</creationdate><recordtype>book_chapter</recordtype><recordid>eNpNkD1PwzAYhM2XRCmd-ANZGBgCfvP6Ix6hlIJUwQKzZcd2GmiTyG5V9d-TqgzccsM9OumOkBug90CpfAAQUFJe6qI8IVfIGUUQQsIpGQ0J5IhMnZGJkuUxYwWX52REkRa5kgwvySSlbzoIQSqpRkTNWh_rff5kknfZdLVNGx-bts66kM2j6Zcp2zWbZfbetdu2CV1cZ8--jt6na3IRzCr5yZ-PydfL7HP6mi8-5m_Tx0XeF1xscrCWV9Z7g8CZqKiztEJjXOC2oExaaq1zKjhUoALYwDBwV4JghbLccoFjcnvs7U2qzCpE01ZN0n1s1ibuNSiFUiIbuLsjl_rDAB-17bqfpIHqw3f633f4C-DXW2s</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Noack, Andreas</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>Energy-Based Clustering of Graphs with Nonuniform Degrees</title><author>Noack, Andreas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p256t-1bb5cbeea31546c0db0c3aadf5b2047b0bbdd9fd3919f1bf43f5d816429b5b563</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Cluster Criterion</topic><topic>Computer science; control theory; systems</topic><topic>Energy Model</topic><topic>Exact sciences and technology</topic><topic>Graph Cluster</topic><topic>Information retrieval. Graph</topic><topic>Information systems. Data bases</topic><topic>Large Graph</topic><topic>Memory organisation. Data processing</topic><topic>Node Versus</topic><topic>Software</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Noack, Andreas</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Noack, Andreas</au><au>Nikolov, Nikola S.</au><au>Healy, Patrick</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Energy-Based Clustering of Graphs with Nonuniform Degrees</atitle><btitle>Graph Drawing</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2006</date><risdate>2006</risdate><spage>309</spage><epage>320</epage><pages>309-320</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540314257</isbn><isbn>3540314253</isbn><eisbn>3540316671</eisbn><eisbn>9783540316671</eisbn><abstract>Widely varying node degrees occur in software dependency graphs, hyperlink structures, social networks, and many other real-world graphs. 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| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Graph Drawing, 2006, p.309-320 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19937734 |
| source | Springer Books |
| subjects | Applied sciences Cluster Criterion Computer science control theory systems Energy Model Exact sciences and technology Graph Cluster Information retrieval. Graph Information systems. Data bases Large Graph Memory organisation. Data processing Node Versus Software Theoretical computing |
| title | Energy-Based Clustering of Graphs with Nonuniform Degrees |
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