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- Title
Community-based diffusion scheme using Markov chain and spectral clustering for mobile social networks.
- Authors
Ryu, Jegwang; Park, Jiho; Lee, Junyeop; Yang, Sung-Bong
- Abstract
With the increase in the number of mobile devices such as tablets and smart watches, mobile social networks (MSNs) provide great opportunities for people to exchange information. As a result, information diffusion has become a critical issue in the emerging MSNs. In this paper, we address the problem of finding the top-k influential users who can effectively spread information in a network, which is referred to as the diffusion minimization problem. In order to minimize the spreading period, we can utilize the k-center problem, but which has a time complexity of NP-hard. We propose a community-based diffusion scheme using Markov chain and spectral clustering (CDMS) to minimize the spreading time by adopting a community concept based on the geographic regularity of human mobility in the MSNs. We exploit the Markov chain to predict a node's mobility patterns and cluster the predicted patterns using the spectral graph theory. Finally, we select the top-k influential nodes in each community. Simulations are performed using the NS-2, based on the home-cell community-based mobility model, to show that the proposed scheme results in MSNs. In addition, we demonstrate that CDMS outperforms the noncommunity-based algorithms in terms of the number of nodes and ratio of k influential nodes.
- Subjects
CELL phones; SOCIAL networks; MARKOV processes; COMPUTER algorithms; INFORMATION sharing
- Publication
Wireless Networks (10220038), 2019, Vol 25, Issue 2, p875
- ISSN
1022-0038
- Publication type
Article
- DOI
10.1007/s11276-017-1599-6