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 ?nding in?uential 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 in?uential 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 in?uential nodes by a dynamic programming. Performance is to be increased by considering the in?uence propagation supported communities and take into account the in?uence propagation crossing communities. Experiments on real large-scale mobile social networks show that the proposed algorithm is quicker than previous algorithms.

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