Particle competition for complex network community detection.
In many real situations, randomness is considered to be uncertainty or even confusion which impedes human beings from making a correct decision. Here we study the combined role of randomness and determinism in particle dynamics for complex network community detection. In the proposed model, particles walk in the network and compete with each other in such a way that each of them tries to possess as many nodes as possible. Moreover, we introduce a rule to adjust the level of randomness of particle walking in the network, and we have found that a portion of randomness can largely improve the community detection rate. Computer simulations show that the model has good community detection performance and at the same time presents low computational complexity.
Quiles
Marcos G
MG
Institute of Mathematics and Computer Science, University of São Paulo, 13560-970, São Carlos, Brazil.
Zhao
Liang
L
Alonso
Ronaldo L
RL
Romero
Roseli A F
RA
D000465:Algorithms; D003198:Computer Simulation; D003657:Decision Making; D003661:Decision Support Techniques; D015233:Models, Statistical; D017711:Nonlinear Dynamics