Title: The discovery in uncertain-social-relationship communities of opportunistic network

Authors: Gang Xu; Jia-Yi Wang; Hai-He Jin; Peng-Fei Mu

Addresses: College of Computer Science, Inner Mongolia University, Huhhot, 010021, China ' College of Computer Science, Inner Mongolia University, Huhhot, 010021, China ' College of Computer Science, Inner Mongolia University, Huhhot, 010021, China ' College of Computer Science, Inner Mongolia University, Huhhot, 010021, China

Abstract: In the current studies of community division of the opportunistic network, we always take the uncertain social relations as the input. In the practical application scenarios, because communications are always disturbed and the movements of nodes are random, the social relations are in the uncertain states. Therefore, the result of the community division based on the certain social relations is impractical. To solve the problem which cannot get the accurate communities under the uncertain social relations, we propose an uncertain-social-relation model of the opportunistic network in this paper. Meanwhile, we analyse the probability distribution of the uncertain social relation and propose an algorithm of the community division based on the social cohesion, and then we divide communities by the uncertain social relations of opportunistic network. The experimental result shows that the K-CLIQUE algorithm of the community division based on the social cohesion is more in accord with practical communities than the traditional K-CLIQUE algorithm of community division.

Keywords: opportunistic network uncertain social relations; K-CLIQUE algorithm; social cohesion; key node.

DOI: 10.1504/IJCSE.2019.103248

International Journal of Computational Science and Engineering, 2019 Vol.20 No.1, pp.40 - 48

Received: 19 Dec 2016
Accepted: 23 Apr 2017
Published online: 23 Oct 2019 *

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