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.
| Original language | English |
|---|---|
| Pages (from-to) | 875-887 |
| Number of pages | 13 |
| Journal | Wireless Networks |
| Volume | 25 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2019 Feb 15 |
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All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Networks and Communications
- Electrical and Electronic Engineering
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Community-based diffusion scheme using Markov chain and spectral clustering for mobile social networks. / Ryu, Jegwang; Park, Jiho; Lee, Junyeop; Yang, Sung-Bong.
In: Wireless Networks, Vol. 25, No. 2, 15.02.2019, p. 875-887.Research output: Contribution to journal › Article
TY - JOUR
T1 - Community-based diffusion scheme using Markov chain and spectral clustering for mobile social networks
AU - Ryu, Jegwang
AU - Park, Jiho
AU - Lee, Junyeop
AU - Yang, Sung-Bong
PY - 2019/2/15
Y1 - 2019/2/15
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85031798238&partnerID=8YFLogxK
U2 - 10.1007/s11276-017-1599-6
DO - 10.1007/s11276-017-1599-6
M3 - Article
AN - SCOPUS:85031798238
VL - 25
SP - 875
EP - 887
JO - Wireless Networks
JF - Wireless Networks
SN - 1022-0038
IS - 2
ER -