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Community Detection in Networks Using Page Rank Vectors

Authors: V.Greeshma; K.Suvarna Vani;

Community Detection in Networks Using Page Rank Vectors

Abstract

Nodes in the real world networks organize in the form of network communities. A community (also referred to as module or cluster)is defined as where the links are denser inside the nodes and sparser outside the nodes in the network. Communities in the networks also overlap because the nodes may belong to different clusters at once. The task of detecting communities in networks becomes an open problem because of lack of reliable algorithms. In practice all the existing community detection methods work good for nonoverlapping communities and fail to detect communities with dense overlaps. We developed a novel method for detecting communities by considering a single seed node. This method successfully captures the overlapping networks ranging from social to information and from biological to citation networks. We believe that the proposed system works well for the overlapping communities.

Keywords

Network Communities, Overlapping communities, seed node, random walk, page rank nibble., Network Communities, Overlapping communities, seed node, random walk, page rank nibble

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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