|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 122 - Issue 19 |
| Published: July 2015 |
| Authors: Smita Bhosale, Dhanshree Kulkarni |
10.5120/21810-5133
|
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.