Biangular coordinates - Revision history

208.50.124.65: /* References */ more complete info on a reference

2014-08-14T23:25:33Z

References: more complete info on a reference

← Older revision		Revision as of 01:25, 15 August 2014
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	The ~~'''Girvan–Newman algorithm''' (named after Michelle Girvan and [[Mark Newman]])~~ is ~~a hierarchical method used to detect [[Community structure\|communities]] in [[complex system]]s~~.<ref name=newman>Girvan M. and Newman M. E. J., [http://dx.doi.org/10.1073/pnas.122653799 Community structure in social and biological networks], Proc. Natl. Acad. Sci. USA '''99''', 7821–7826 (2002)</ref>		The author is called Araceli Gulledge. Years in the past we moved to Kansas. The job he's been occupying for many years is a messenger. One of my favorite hobbies is tenting and now I'm trying to make money with it.<br><br>Also visit my blog post - [http://www.shownetbook.com/enone/xe/?document_srl=3029968 http://www.shownetbook.com]

	~~== Edge betweenness and community structure ==~~
	The Girvan–Newman algorithm detects communities by progressively removing edges from the original network. The connected components of the remaining network are the comunnities. Instead of trying to construct a measure that tells us which edges are the most central to communities, the Girvan–Newman algorithm focuses on edges that are most likely "between" communities.

	~~[[Betweenness centrality\|Vertex betweenness]] has been studied~~ in the past as a measure of the [[centrality]] and influence of nodes in networks. For any node <math>i</math>, vertex betweenness is defined as the number of shortest paths between pairs of nodes that run through it. It is a measure of the influence of a node over the flow of information between other nodes, especially in cases where information flow over a network primarily follows the shortest available path. The Girvan–Newman algorithm extends this definition to the case of edges, defining the "edge betweenness" of an edge as the number of shortest paths between pairs of nodes that run along it. If there is more than one shortest path between a pair of nodes, each path is assigned equal weight such that the total weight of all of the paths is equal to ~~unity~~. If a network contains communities or groups that are only loosely connected by a few intergroup edges, then all shortest paths between different communities must go along one of these few edges. Thus, the edges connecting communities will have high edge betweenness (at least '''one''' of them). By removing these edges, the groups are separated from one another and so the underlying community structure of the network is revealed.

	The ~~algorithm~~'s ~~steps~~ for ~~community detection are summarized below~~

	~~# The betweenness of all existing edges in the network~~ is ~~calculated first~~.
	~~# The edge with the highest betweenness is removed.~~
	~~# The betweenness~~ of ~~all edges affected by the removal~~ is ~~recalculated.~~
	~~# Steps 2~~ and ~~3 are repeated until no edges remain.~~

	~~The fact that the only betweennesses being recalculated are only the ones which are affected by the removal, may lessen the running time of the process~~' ~~simulation in computers. However, the betweenness centrality must be recalculated~~ with each step, or severe errors occur. The reason is that the network adapts itself to the new conditions set after the edge removal. For instance, if two communities are connected by more than one edge, then there is no guarantee that '''all''' of these edges will have high betweenness. According to the method, we know that '''at least one''' of them will have, but nothing more than that is known. By recalculating betweennesses after the removal of each edge, it ~~is ensured that at least one of the remaining edges between two communities will always have a high value~~.

	The end result of the Girvan–Newman algorithm is a [[dendrogram]]. As the Girvan–Newman algorithm runs, the dendrogram is produced from the top down (i.e. the network splits up into different communities with the successive removal of links). The leaves of the dendrogram are individual nodes.

	~~== See also ==~~
	* [[Betweenness]]
	* [[Closeness (mathematics)\|Closeness]]
	* [[Hierarchical clustering]]
	* [[Modularity (networks)\|Modularity]]

	~~== References ==~~

	<~~references/~~>

	~~{{DEFAULTSORT:Girvan~~-~~Newman algorithm}}~~
	[~~[Category~~:~~Graph algorithms]]~~
	~~[[Category~~:~~Networks]]~~
	~~[[Category:Network analysis]~~]

en>Addbot: Bot: Migrating 3 interwiki links, now provided by Wikidata on d:q1014373

2013-03-02T06:31:26Z

Bot: Migrating 3 interwiki links, now provided by Wikidata on d:q1014373

← Older revision		Revision as of 08:31, 2 March 2013
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	~~Hi there~~. My name is ~~Sophia Meagher even though~~ it is ~~not~~ the ~~title on my beginning certification~~. He is ~~an information officer~~. ~~To climb~~ is ~~something I truly enjoy performing~~. ~~North Carolina~~ is the ~~location he enjoys most~~ but ~~now he~~ is ~~contemplating other options~~.~~<br><br>my blog post accurate psychic predictions (~~[~~http://203~~.~~250~~.78.~~160/zbxe/?document_srl=1792908 mouse click~~ the up ~~coming website~~])		The '''Girvan–Newman algorithm''' (named after Michelle Girvan and [[Mark Newman]]) is a hierarchical method used to detect [[Community structure\|communities]] in [[complex system]]s.<ref name=newman>Girvan M. and Newman M. E. J., [http://dx.doi.org/10.1073/pnas.122653799 Community structure in social and biological networks], Proc. Natl. Acad. Sci. USA '''99''', 7821–7826 (2002)</ref>

			== Edge betweenness and community structure ==
			The Girvan–Newman algorithm detects communities by progressively removing edges from the original network. The connected components of the remaining network are the comunnities. Instead of trying to construct a measure that tells us which edges are the most central to communities, the Girvan–Newman algorithm focuses on edges that are most likely "between" communities.

			[[Betweenness centrality\|Vertex betweenness]] has been studied in the past as a measure of the [[centrality]] and influence of nodes in networks. For any node <math>i</math>, vertex betweenness is defined as the number of shortest paths between pairs of nodes that run through it. It is a measure of the influence of a node over the flow of information between other nodes, especially in cases where information flow over a network primarily follows the shortest available path. The Girvan–Newman algorithm extends this definition to the case of edges, defining the "edge betweenness" of an edge as the number of shortest paths between pairs of nodes that run along it. If there is more than one shortest path between a pair of nodes, each path is assigned equal weight such that the total weight of all of the paths is equal to unity. If a network contains communities or groups that are only loosely connected by a few intergroup edges, then all shortest paths between different communities must go along one of these few edges. Thus, the edges connecting communities will have high edge betweenness (at least '''one''' of them). By removing these edges, the groups are separated from one another and so the underlying community structure of the network is revealed.

			The algorithm's steps for community detection are summarized below

			# The betweenness of all existing edges in the network is calculated first.
			# The edge with the highest betweenness is removed.
			# The betweenness of all edges affected by the removal is recalculated.
			# Steps 2 and 3 are repeated until no edges remain.

			The fact that the only betweennesses being recalculated are only the ones which are affected by the removal, may lessen the running time of the process' simulation in computers. However, the betweenness centrality must be recalculated with each step, or severe errors occur. The reason is that the network adapts itself to the new conditions set after the edge removal. For instance, if two communities are connected by more than one edge, then there is no guarantee that '''all''' of these edges will have high betweenness. According to the method, we know that '''at least one''' of them will have, but nothing more than that is known. By recalculating betweennesses after the removal of each edge, it is ensured that at least one of the remaining edges between two communities will always have a high value.

			The end result of the Girvan–Newman algorithm is a [[dendrogram]]. As the Girvan–Newman algorithm runs, the dendrogram is produced from the top down (i.e. the network splits up into different communities with the successive removal of links). The leaves of the dendrogram are individual nodes.

			== See also ==
			* [[Betweenness]]
			* [[Closeness (mathematics)\|Closeness]]
			* [[Hierarchical clustering]]
			* [[Modularity (networks)\|Modularity]]

			== References ==

			<references/>

			{{DEFAULTSORT:Girvan-Newman algorithm}}
			[[Category:Graph algorithms]]
			[[Category:Networks]]
			[[Category:Network analysis]]

en>Ripchip Bot: r2.7.1) (robot Modifying: cs:Biangulární souřadnice

2011-04-21T10:23:41Z

r2.7.1) (robot Modifying: cs:Biangulární souřadnice

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Hi there. My name is Sophia Meagher even though it is not the title on my beginning certification. He is an information officer. To climb is something I truly enjoy performing. North Carolina is the location he enjoys most but now he is contemplating other options.<br><br>my blog post accurate psychic predictions ([http://203.250.78.160/zbxe/?document_srl=1792908 mouse click the up coming website])