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Keywords
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Influence Maximization Problem, Information Diffusion, Influence Spread, Graph Topology, Centrality
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Abstract
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Influence maximization is a problem based on diffusion and probability in social networks with the aim of finding the least k node with the most influence. These nodes play an essential role in the diffusion process. However, the influence maximization problem faces two essential challenges of time efficiency and optimal selection of the seed nodes. To solve these challenges, we proposed an algorithm based on the properties of the graph topology structure and centrality, called IMT (Influence Maximization based on the Topology) algorithm. This algorithm selects the seed nodes from the dense part of the graph that can access more nodes in the shortest distance. Finally, experiments showed that the proposed algorithm outperformed the other algorithms in terms of influence spread and running time.
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