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Abstract
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Influence maximization is the process of identifying a small set of influential nodes from a complex network to
maximize the number of activation nodes. Due to the critical issues such as accuracy, stability, and time
complexity in selecting the seed set, many studies and algorithms has been proposed in recent decade. However,
most of the influence maximization algorithms run into major challenges such as the lack of optimal seed nodes
selection, unsuitable influence spread, and high time complexity. In this paper intends to solve the mentioned
challenges, by decreasing the search space to reduce the time complexity. Furthermore, It selects the seed nodes
with more optimal influence spread concerning the characteristics of a community structure, diffusion capability
of overlapped and hub nodes within and between communities, and the probability coefficient of global diffusion.
The proposed algorithm, called the FIP algorithm, primarily detects the overlapping communities, weighs
the communities, and analyzes the emotional relationships of the community’s nodes. Moreover, the search
space for choosing the seed nodes is limited by removing insignificant communities. Then, the candidate nodes
are generated using the effect of the probability of global diffusion. Finally, the role of important nodes and the
diffusion impact of overlapping nodes in the communities are measured to select the final seed nodes. Experimental
results in real-world and synthetic networks indicate that the proposed FIP algorithm has significantly
outperformed other algorithms in terms of efficiency and runtime.
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