A Census-Based Genetic Algorithm for Target Set Selection Problem in Social Networks

TL;DR

This paper introduces a novel census-based genetic algorithm for the Target Set Selection problem in social networks, achieving optimal solutions and improving solution sizes on real-world graphs by leveraging census data for diversity and parallel processing.

Contribution

The paper presents a new census-based genetic algorithm that enhances diversity and solution quality for TSS in social networks, with successful experiments on real-world graphs.

Findings

Achieved optimal solutions in all tested cases.

Improved solution size by approximately 9.57 on average.

Successfully applied to large real-world social network graphs.

Abstract

This paper considers the Target Set Selection (TSS) Problem in social networks, a fundamental problem in viral marketing. In the TSS problem, a graph and a threshold value for each vertex of the graph are given. We need to find a minimum size vertex subset to "activate" such that all graph vertices are activated at the end of the propagation process. Specifically, we propose a novel approach called "a census-based genetic algorithm" for the TSS problem. In our algorithm, we use the idea of a census to gather and store information about each individual in a population and collect census data from the individuals constructed during the algorithm's execution so that we can achieve greater diversity and avoid premature convergence at locally optimal solutions. We use two distinct census information: (a) for each individual, the algorithm stores how many times it has been identified during the execution (b) for each network node, the algorithm counts how many times it has been included in a solution. The proposed algorithm can also self-adjust by using a parameter specifying the aggressiveness employed in each reproduction method. Additionally, the algorithm is designed to run in a parallelized environment to minimize the computational cost and check each individual's feasibility. Moreover, our algorithm finds the optimal solution in all cases while experimenting on random graphs. Furthermore, we execute the proposed algorithm on 14 large graphs of real-life social network instances from the literature, improving around 9.57 solution size (on average) and 134 vertices (in total) compared to the best solutions obtained in previous studies.