Anderson, Rodney

English

Furman University

+The Girvan–Newman algorithm detects communities by progressively removing edges from the original network. The connected components of the remaining network are the communities. 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. Communities are clusters of tightly connected nodes.Introduction Micro-Media Vertex BetweennessImplementationeasily modifiableConclusion.This is the first method of detecting node“centrality” within a network. Vertexbetweenness is relevant in situations wherethe network modulates transfer of goodsbetween known START and END points. Thatis if the goods are looking for the shortestpath.Network ClusterAs mentioned before the Girvan-Newman Algorithm seeks todetect which nodes are the ones with the most frequent“through” activity that connects communities. The top leftdiagram shows the overlay of a structured network. And thebottom left image shows the path that connects thosecommunities, in which case the Girvan – Newman Algorithmdeletes that node and repeats the search for the node with highdensity until the underlying structure of the network is revealed.This method is used to seek out the central node between communities. This canbe used in many aspects of everyday life. For example this generations favoritepast time, social media. Within social media an analyzer can determine whereusers interact with each other the most. Prime example is Facebook. WithFacebook there are many aspects of letting the world know your status, who yourfriends are, what events you are attending and so fourth. The betweennesscomes into play when we get to communicating, or Facebook messenger. AsFacebook has a messaging option on its site, it was noticed that many peopleutilize this area of the site, high density. Therefore pushing to create a mobilemassager app that takes away the middle man, the Facebook website in generaland giving users a straight path to socializing with their friends.The Insight to the Girvan-NewmanAlgorithm: “Detecting Communities in Network Systems”Rodney Anderson \The goal of finding the most dense nodes is alsounderway in many other areas such as marketingand advertisement. As the top graph shows theaction of gambling and how much it takes place andbreaks that down into not only gambling, but whattypes of gambling is taking place, and in thisexample it is Sports betting. The second figureshows social circles and connections betweenpeople on Facebook, linking friends, family,classmates and more.Hierarchical Method This algorithm uses what is known as ahierarchical method, meaning from the TopDown, to detect these communities incomplex network systems. Known asDivisive, meaning all observations start inone cluster, and splits are performedrecursively as one moves down thehierarchy. These results usually are shown inwhat is known as a Dendrogram. ADendrogram is a “tree” diagram that showsthe hierarchical clustering, frequently usedin biology to show clusters of gene samples.Network Cluster example Detecting the between nodeMicro-Marketing in a sponsored search  

The Insight to the Girvan-Newman Algorithm:  Detecting Communities in Network Systems

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The Insight to the Girvan-Newman Algorithm: Detecting Communities in Network Systems

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