| Summary: | 碩士 === 淡江大學 === 資訊工程學系碩士班 === 105 === Since the surge of the popularity of social network, recently, there has been a tremendous wave of interest in the investigation of influence maximization problem. Given a social network structure, the problem of influence maximization is to determine a minimum set of nodes that could maximize the spread of influences. Nowadays, due to the dramatic size growing of social network, the efficiency and scalability of algorithms for influence maximization become more and more crucial. Although many recent studies have focused on the problem of influence maximization, these works, in general, are time consuming when a large-scale social network is given. In this paper, by exploiting potential community structure, we develop an efficient algorithm EIM (standing for Efficient Influence Maximization) that reduces the execution time and memory usage while guarantee the accuracy of results. The experimental results on real datasets indicate that our algorithms not only significantly outperform state-of-the-art algorithms in efficiency but also possess graceful scalability.
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