DocumentCode
3155225
Title
A Method for Local Community Detection by Finding Core Nodes
Author
Tiantian Zhang ; Bin Wu
Author_Institution
Sch. of Comput., Beijing Univ. of Posts & Telecommun., Beijing, China
fYear
2012
fDate
26-29 Aug. 2012
Firstpage
1171
Lastpage
1176
Abstract
Currently, the detection of global community structure in networks has gathered a lot of attention. Most of the methods need global knowledge of the graphs which would be unrealistic to get when the graphs are too large or evolve too quickly. Moreover, sometimes we are only interested in the community structures of some given nodes, not all nodes. So detecting the community of a given node i.e. local community detection is more appropriate. Most of the proposed solutions for local community detection built upon the source nodes are sensitive to the position of source nodes. In this paper, we propose a method to detect local community of a given node by finding the core node of the community firstly. Then expand the core node´s cliques to get community of the given node. We validate our method on real-world networks whose community structures are available. The result shows that our method can get high recall and precision score and is quite effective and flexible to identify local communities, irrespective of the source node position.
Keywords
graph theory; network theory (graphs); core node clique expansion; core node finding; global community structure detection; graph global knowledge; local community detection; precision score; real-world networks; recall score; source nodes; Blogs; Communities; Educational institutions; Extraterrestrial measurements; Image edge detection; Knowledge engineering; Social network services; Local community detection; clique; core node; strength of relattion;
fLanguage
English
Publisher
ieee
Conference_Titel
Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on
Conference_Location
Istanbul
Print_ISBN
978-1-4673-2497-7
Type
conf
DOI
10.1109/ASONAM.2012.202
Filename
6425598
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