Multi-Objective Influence Maximization Under Varying-Size Solutions and Constraints

Abstract

Identification of a set of influential spreaders in a network, called the Influence Maximization (IM) problem, has gained much popularity due to its immense practicality. In real-life applications, not only the influence spread size, but also some other criteria such as the selection cost and the size of the seed set play an important role in selecting the optimal solution. However, majority of the existing works have treated this issue as a single-objective optimization problem, where decision-makers are forced to make their choices regarding other variables in advance despite having a thorough understanding of them. This research formulates a multi-objective version of the IM problem (referred to as MOIMP), which considers three competing objectives while subject to certain practical restrictions. Theoretical analysis reveals that the influence spreading function under the suggested MOIMP framework is no longer monotone, but submodular. We also considered three well-established multi-objective evolutionary algorithms to solve the proposed MOIMP. Since the proposed MOIMP addresses varying-size seeds, all the considered algorithms are significantly modified to fit into it. Experimental results on four real-life datasets, evaluating and comparing the performance of the considered algorithms, demonstrate the effectiveness of the proposed MOIMP.