Authors: Fei Shang; Xiaobo Nie
Addresses: Department of Industrial Engineering, Mechanics Institute, Inner Mongolia University of Technology, Hohhot, Inner Mongolia, 010051, China ' Department of Industrial Engineering, Mechanics Institute, Inner Mongolia University of Technology, Hohhot, Inner Mongolia, 010051, China
Abstract: Most community discovery methods are based on network topology and edge density for best community determination, but these methods have very high computational complexity and are very sensitive to the form and type of network. In order to solve these problems, this paper proposes a micro-blogging community interaction optimisation algorithm based on dynamic node adaptive increment model, which is based on optimising the interaction of members in each community, and uses greedy algorithm to search the best candidate for the optimal community effectively without traversing all nodes. The model can quickly and accurately measure the interaction difference between the community and the community. Finally, the simulation tests on the datum test network and the Sohu micro-blogging platform show that the proposed algorithm is better than the selected contrast algorithm in the index of recall, accuracy, algorithm calculation time and network coverage.
Keywords: complex network; edge density; community discovery; self-adaptive; interaction optimisation; incremental model.
International Journal of Innovative Computing and Applications, 2020 Vol.11 No.2/3, pp.115 - 122
Received: 08 Mar 2019
Accepted: 29 Apr 2019
Published online: 30 Apr 2020 *