Local Algorithms for Graphs
We are going to analyze local algorithms over sparse random graphs. These algorithms are based on local information where local regards to a decision made by the exploration of a small neighbourhood of a certain vertex plus a believe of the structure of the whole graph and maybe added some randomnes...
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| creator | Gamarnik, David Hemery, Mathieu Hetterich, Samuel |
| description | We are going to analyze local algorithms over sparse random graphs. These
algorithms are based on local information where local regards to a decision
made by the exploration of a small neighbourhood of a certain vertex plus a
believe of the structure of the whole graph and maybe added some randomness.
This kind of algorithms can be a natural response to the given problem or an
efficient approximation such as the Belief Propagation Algorithm. |
| doi_str_mv | 10.48550/arxiv.1409.5214 |
| format | Article |
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algorithms are based on local information where local regards to a decision
made by the exploration of a small neighbourhood of a certain vertex plus a
believe of the structure of the whole graph and maybe added some randomness.
This kind of algorithms can be a natural response to the given problem or an
efficient approximation such as the Belief Propagation Algorithm.</description><identifier>DOI: 10.48550/arxiv.1409.5214</identifier><language>eng</language><subject>Computer Science - Data Structures and Algorithms ; Physics - Disordered Systems and Neural Networks</subject><creationdate>2014-09</creationdate><rights>http://creativecommons.org/licenses/by-nc-sa/3.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1409.5214$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1409.5214$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Gamarnik, David</creatorcontrib><creatorcontrib>Hemery, Mathieu</creatorcontrib><creatorcontrib>Hetterich, Samuel</creatorcontrib><title>Local Algorithms for Graphs</title><description>We are going to analyze local algorithms over sparse random graphs. These
algorithms are based on local information where local regards to a decision
made by the exploration of a small neighbourhood of a certain vertex plus a
believe of the structure of the whole graph and maybe added some randomness.
This kind of algorithms can be a natural response to the given problem or an
efficient approximation such as the Belief Propagation Algorithm.</description><subject>Computer Science - Data Structures and Algorithms</subject><subject>Physics - Disordered Systems and Neural Networks</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzk8LgjAcxvFdOoR1D4LwDWi_6TbdUaR_IHTxLr_NLQVFmRH17svq9MD38PAhZEMhZCnnsEf3bB8hZSBDHlG2JNti0Nj5WXcbXHtv-sm3g_NPDsdmWpGFxW4y6_96pDweyvwcFNfTJc-KAAVnARNKpRSE4jWiSTQoBZZFwgojkX4CR8oTCbw2pkYdSxpTATJJpQarozT2yO53-8VVo2t7dK9qRlYzMn4D4V01oA</recordid><startdate>20140918</startdate><enddate>20140918</enddate><creator>Gamarnik, David</creator><creator>Hemery, Mathieu</creator><creator>Hetterich, Samuel</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20140918</creationdate><title>Local Algorithms for Graphs</title><author>Gamarnik, David ; Hemery, Mathieu ; Hetterich, Samuel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a654-46bb8106b5daae7c0bb0f426f6e9a1e7c5a157905deedac39131609789c0fc283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Computer Science - Data Structures and Algorithms</topic><topic>Physics - Disordered Systems and Neural Networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Gamarnik, David</creatorcontrib><creatorcontrib>Hemery, Mathieu</creatorcontrib><creatorcontrib>Hetterich, Samuel</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gamarnik, David</au><au>Hemery, Mathieu</au><au>Hetterich, Samuel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Local Algorithms for Graphs</atitle><date>2014-09-18</date><risdate>2014</risdate><abstract>We are going to analyze local algorithms over sparse random graphs. These
algorithms are based on local information where local regards to a decision
made by the exploration of a small neighbourhood of a certain vertex plus a
believe of the structure of the whole graph and maybe added some randomness.
This kind of algorithms can be a natural response to the given problem or an
efficient approximation such as the Belief Propagation Algorithm.</abstract><doi>10.48550/arxiv.1409.5214</doi><oa>free_for_read</oa></addata></record> |
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| identifier | DOI: 10.48550/arxiv.1409.5214 |
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| source | arXiv.org |
| subjects | Computer Science - Data Structures and Algorithms Physics - Disordered Systems and Neural Networks |
| title | Local Algorithms for Graphs |
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