Influence Maximization on Mobile Social Network using Location based Community Greedy Algorithm
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10.5120/21810-5133 |
Smita Bhosale and Dhanshree Kulkarni. Article: Influence Maximization on Mobile Social Network using Location based Community Greedy Algorithm. International Journal of Computer Applications 122(19):28-31, July 2015. Full text available. BibTeX
@article{key:article,
author = {Smita Bhosale and Dhanshree Kulkarni},
title = {Article: 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},
month = {July},
note = {Full text available}
}
Abstract
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.
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