Title :
A mining algorithm for overlapping community structure in networks
Author :
Nan, Lu ; Xiao-Gang, Peng ; Lei, Qin
Author_Institution :
Coll. of Software, Shenzhen Univ., Shenzhen, China
Abstract :
Nowadays, most existing community detecting algorithms treat smaller communities within a society network independently to reduce the complexity. Yet in most real world cases, those communities are always interleaved and overlapping. To address this problem, a novel algorithm based on greedy algorithm, namely MA-OCS, is proposed, which adopts agglomerative method to extract overlapped communities within a society network. Experiments on different standard datasets indicate that the proposed algorithm excels existing GN and KL algorithms in speed and accuracy.
Keywords :
computational complexity; data mining; greedy algorithms; network theory (graphs); social networking (online); MA-OCS algorithm; agglomerative method; community detecting algorithms; complexity reduction; greedy algorithm; overlapped community extraction; overlapping community structure mining algorithm; social networks; society network; standard datasets; Algorithm design and analysis; Communities; Image edge detection; Agglomerative method; Community structure; Greedy algorithm; Overlapping modularity; Social networks;
Conference_Titel :
Information Society (i-Society), 2012 International Conference on
Conference_Location :
London
Print_ISBN :
978-1-4673-0838-0