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

2024-10-02

arXiv.org

AI Summary

This paper addresses the Target Set Selection (TSS) problem in social networks: given a graph and node-specific activation thresholds, identify the smallest initial seed set such that cascading activation eventually covers the entire graph. We propose a census-based genetic algorithm that innovatively incorporates dual-dimensional frequency statistics—tracking both individual solution recurrence in the population history and node selection frequency across candidate solutions—coupled with adaptive aggressiveness control. This design effectively preserves population diversity, mitigates premature convergence, and natively supports parallelization. Evaluated on 14 real-world social network datasets, our method reduces average solution size by 9.57% compared to prior state-of-the-art approaches and achieves an additional 134 nodes covered overall. Moreover, it consistently finds optimal solutions across all synthetic random graph instances, demonstrating both effectiveness and robustness.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Social mining and social search on the Web
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• Minimize vertex subset to activate entire social network
• Propose census-based genetic algorithm for TSS problem
• Improve solution size and diversity in viral marketing

Innovation

• Census-based genetic algorithm enhances diversity
• Self-adjusting aggressiveness in reproduction methods
• Parallelized execution reduces computational cost