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Proliferation of social network service permeates lives of people on internet, and its social, political and cultural significance prompts the need to understand and to analyze the contents and structures of such services. The sheer volume of such social network is enormous, and it necessitates development and implementation of efficient social network analysis algorithms. Among these, Influence Maximization is one example of such algorithm. The objective of the influence maximization algorithm is to find a small subset of nodes, so-called seed-nodes, that result in maximization of the spread of influence through the edges in the graph which represents connections in social network. As the cost-efficient, high-performance computing power of many-core GPUs is widely utilized in nearly all areas of computing, we apply our expertise in GPU parallelization to the influence maximization algorithm. The graph algorithms are known as one of sparse algorithms since its irregular data structure requires indirect accesses to memory, resulting in lowbandwidth memory access. Sparse algorithms are one area where many researchers are focused on its efficient parallelization, because the usage of such algorithms is universal and thus, vital to broad application areas, from scientific simulations to social studies. In this paper, we introduce algorithms to compute influence maximization of social network, and adopt this algorithm to fit parallel implementation on many-core GPU. We also analyze our implementations in terms of factors affecting GPU performance.