International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 122 - Issue 19 |
Published: July 2015 |
Authors: Smita Bhosale, Dhanshree Kulkarni |
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Smita Bhosale, Dhanshree Kulkarni . Influence Maximization on Mobile Social Network using Location based Community Greedy Algorithm. International Journal of Computer Applications. 122, 19 (July 2015), 28-31. DOI=10.5120/21810-5133
@article{ 10.5120/21810-5133, author = { Smita Bhosale,Dhanshree Kulkarni }, title = { Influence Maximization on Mobile Social Network using Location based Community Greedy Algorithm }, journal = { International Journal of Computer Applications }, year = { 2015 }, volume = { 122 }, number = { 19 }, pages = { 28-31 }, doi = { 10.5120/21810-5133 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2015 %A Smita Bhosale %A Dhanshree Kulkarni %T Influence Maximization on Mobile Social Network using Location based Community Greedy Algorithm%T %J International Journal of Computer Applications %V 122 %N 19 %P 28-31 %R 10.5120/21810-5133 %I Foundation of Computer Science (FCS), NY, USA
A mobile social network plays an important role as the spread of information and influence in the form of "word-of-mouth". It is basic thing to find small set of influential people in a mobile social network such that targeting them initially. It will increase the spread of the influence . The problem of finding the most influential nodes in network is NP-hard. It has been shown that a Greedy algorithm with provable approximation guarantees can give good approximation. Community based Greedy algorithm is used for mining top-K influential nodes. It has two components: dividing the mobile social network into several communities by taking into account information diffusion and selecting communities to find influential nodes by a dynamic programming. Location Based community Greedy algorithm is used to find the influence node based on Location and consider the influence propagation within Particular area. Experiments result on real large-scale mobile social networks show that the proposed location based greedy algorithm has higher efficiency than previous community greedy algorithm.