TSIFIM: A three-stage iterative framework for influence maximization in complex networks

The problem of influence maximization is a classic issue that has been well-studied in the field of network science, but most of existing researches are compromising among computational complexity or result accuracy. In this work, a three-stage iterative framework for influence maximization (TSIFIM) is presented to find a set of seed spreaders in complex networks. In TSIFIM, the initial candidate seeds are first selected by considering the global communicability of each node and its importance in their local network. Then, in addition to the candidate seeds, other remained nodes are assigned to the specific communities based on the proposed local resource allocation similarity index, and the core node in each community which satisfies the local influence threshold condition are selected as the supplementary candidate seeds. Furthermore, we employ an adaptive search strategy to find the optimal solution among these candidates. The proposed algorithm is compared with eight popular influence maximization algorithms on nine real-world networks to verify the performance. Experimental results show that TSIFIM has better performance in terms of influence spreading, sensitivity analysis, seed dispersion and statistical test.