An MCDM integrated adaptive simulated annealing approach for influence maximization in social networks

Abstract

The Influence Maximization (IM) problem aims to identify a small subset of nodes that have the most influence spread in a network. Although it is an NP-hard problem, the continuous increasing size of the social networks leads to a substantially higher computational complexity, and therefore, has motivated numerous researchers to explore better approaches. This paper proposes a multi-criteria decision making (MCDM) based meta-heuristic approach to solve the IM problem in social networks. An MCDM approach is utilized to select candidate nodes by eliminating less influential ones at the preliminary phase which decreases the computation cost. Thereafter, to find the optimal solution, a modified version of Simulated Annealing (SA) with an enhanced search strategy is proposed. The performance of this proposed approach is tested and verified by solving the IM problem on eight real-life social networks of different sizes and types and comparing the results with six benchmark algorithms. The experimental results indicate that the proposed algorithm has a better trade-off between the solution quality and computational run time than the other algorithms.