A fast overlapping community detection algorithm with self-correcting ability.
Due to the defects of all kinds of modularity, this paper defines a weighted modularity based on the density and cohesion as the new evaluation measurement. Since the proportion of the overlapping nodes in network is very low, the number of the nodes' repeat visits can be reduced by signing the vertices with the overlapping attributes. In this paper, we propose three test conditions for overlapping nodes and present a fast overlapping community detection algorithm with self-correcting ability, which is decomposed into two processes. Under the control of overlapping properties, the complexity of the algorithm tends to be approximate linear. And we also give a new understanding on membership vector. Moreover, we improve the bridgeness function which evaluates the extent of overlapping nodes. Finally, we conduct the experiments on three networks with well known community structures and the results verify the feasibility and effectiveness of our algorithm.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
Laizhong
0000-0003-1991-290X
L
Cui
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
Lei
0000-0002-5468-6437
L
Qin
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
Nan
N
Lu
D000465:Algorithms