DocumentCode :
3717396
Title :
A community detection method based on K-shell
Author :
Yang Wang;Liutong Xu;Bin Wu
Author_Institution :
Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China
fYear :
2015
Firstpage :
2314
Lastpage :
2319
Abstract :
We propose a community detection method based on K-shell. Our method determines some core nodes of the graph according to the K-shell value of these nodes. These core nodes constitute a subgraph on which we use the community detection algorithm to divide the core nodes into communities. Compared to classical methods, by this way, our proposed method removes the non-core nodes which may impact the quality of the solutions, and the running time would be reduced by 45% at most because the graph scale is reduced. Then we use the idea of LPA to infer community labels for the non-core nodes. Our experiments demonstrate that our method can reach the quality of solutions of CNM algorithms on the network constructed by Planted l-partition model which is also better than it on dataset of the Zachary karate club network. Meanwhile, we run our method on large-scale datasets, which has a better performance than the CNM algorithm.
Keywords :
"Peer-to-peer computing","Detection algorithms","Optimization","Time complexity","Complex networks","Big data"
Publisher :
ieee
Conference_Titel :
Big Data (Big Data), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/BigData.2015.7364021
Filename :
7364021
Link To Document :
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