Abstract
Influence maximization algorithms try to select a set of individuals in social networks that are more influential. The Influence maximization problem is important in marketing and many researchers has researched on it and proposed new algorithms. All proposed algorithms are not scalable and are very time consuming for very large social networks generally. In this paper, a fast and scalable influence maximization algorithm called FSIM is proposed based on community detection. FSIM algorithm decreases number of nodes that must be examined without loss of the operations quality therefore it can find seeds quickly. FSIM can maximize influence in large social networks. Experimental results show FSIM is faster and more scalable than existing algorithms.
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