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INFLUENCE MAXIMIZATION ON LARGE-SCALE MOBILE

SOCIAL NETWORK: A DIVIDE-AND-CONQUER METHOD

ABSTRACT

 With the proliferation of mobile devices and wireless technologies, mobile social network systems are increasingly available. A mobile social network plays an essential role as the spread of information and influence in the form of “word-of-mouth”. It is a fundamental issue to find a subset of influential individuals in a mobile social network such that targeting them initially (e.g., to adopt a new product) will maximize the spread of the influence (further adoptions of the new product). The problem of finding the most influential nodes is unfortunately NP-hard. It has been shown that a Greedy algorithm with provable approximation guarantees can give good approximation; However, it is computationally expensive, if not prohibitive, to run the greedy algorithm on a large mobile social network. In this paper, a divide-and-conquer strategy with parallel computing mechanism has been adopted. We first propose an algorithm called Community-based Greedy algorithm for mining top-K influential nodes. It encompasses two components: dividing the large-scale mobile social network into several communities by taking into account information diffusion and selecting communities to find influential nodes by a dynamic programming. Then, to further improve the performance, we parallelize the influence propagation based on communities and consider the influence propagation crossing communities. Also, we give precision analysis to show approximation guarantees of our models. Experiments on real large-scale mobile social networks show that the proposed methods are much faster than previous algorithms, meanwhile, with high accuracy.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING CONCEPT:- In the existing system the problem here is to choose a set of individuals to send the free samples such that they eventually influence the largest number of people in the network. The prohibitive cost of finding influential nodes over the whole network would be reduced greatly if we find influential nodes with regard to communities.	PROPOSED CONCEPT:- In this paper, we tackle the problem of influence maximization in a mobile social network. In the proposed system the basic idea is to exploit the community structure property of social networks, where the networks are weighted directed graphs. According to the fact that a large social network usually appears with local densely connected subsets of nodes while only sparse links exist among different local concentrated regions.
EXISTING TECHNIQUE:- Top-K Influential Nodes	PROPOSED ALGORITHM:- Community Detection, CGA Algorithm
TECHNIQUE DEFNITION:- Previous Community-based Greedy Algorithm to find Top-K influential nodes in communities instead of the whole network. Then, to further improve the efficiency and minimize loss of influence spread led by community detection.	ALGORITHM  DEFNITION:- Community structure is a basic property of social network and communities represent real circles of social groups in which members are more likely to influence each other. We will make use of the detected communities to approximate the influence of nodes in the whole network.
DRAWBACKS:- Difficult to compute desirable node Unpredictable data loss	ADVANTAGES:- It will be more efficient to choose influential nodes within communities. Independent Cascade model. Easy to restrict whole network