A Greedy Algorithm Approach for Mobile Social Network

Abstract

With the proliferation of mobile devices and wireless technologies, mobile social network systems used more. A mobile social network has important role in social network. The Process of finding influential nodes is NP-hard. Greedy rule with demonstrable approximation guarantees will provide smart approximation. A divide-and-conquer method with parallel computing mechanism has been used. Community-based Greedy rule for mining top-K influential nodes is used first. It has two parts: dividing the large-scale mobile social network into many communities by taking under consideration data diffusion. Communities select influential nodes by a dynamic programming. Performance is to be increased by considering the influence propagation supported communities and take into account the influence propagation crossing communities. Experiments on real large-scale mobile social networks show that the proposed algorithm is quicker than previous algorithms. General Terms Mobile social network, influence maximization, PCA greedy algorithm, Keywords PCA-Parallelized Community-based Algorithm