DocumentCode :
1909987
Title :
Overlapping Community Detection by Collective Friendship Group Inference
Author :
Rees, Bradley S. ; Gallagher, Keith B.
Author_Institution :
Dept. of Comput. Sci., Durham Univ., Durham, UK
fYear :
2010
fDate :
9-11 Aug. 2010
Firstpage :
375
Lastpage :
379
Abstract :
There has been considerable interest in improving the capability to identify communities within large collections of social networking data. However, many of the existing algorithms will compartment an actor (node) into a single group, ignoring the fact that in real-world situations people tend to belong concurrently to multiple groups. Our work focuses on the ability to find overlapping communities by aggregating the community perspectives of friendship groups, derived from egonets. We will demonstrate that our algorithm not only finds overlapping communities, but additionally helps identify key members, which bind communities together. Additionally, we will highlight the parallel feature of the algorithm as a means of improving runtime performance.
Keywords :
pattern clustering; social networking (online); collective friendship group inference; egonets; overlapping community detection; runtime performance; social networking data; Communities; Complex networks; Social network services; Network clustering; community detection; complex networks; egonets; social networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference on
Conference_Location :
Odense
Print_ISBN :
978-1-4244-7787-6
Electronic_ISBN :
978-0-7695-4138-9
Type :
conf
DOI :
10.1109/ASONAM.2010.28
Filename :
5562742
Link To Document :
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