Publication Date

Fall 2025

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Katerina Potika

Second Advisor

William Andreopoulos

Third Advisor

Rohan Mohapatra

Keywords

Influence Maximization, Fairness, Social Network Analysis, Gini Coefficient, Multi-objective Optimization, Genetic Algorithms, Pareto Front, Information Dissemination

Abstract

Influence Maximization (IM) focuses on identifying a small set of influential nodes (seed nodes) in social networks to maximize the spread of information, ideas, or behaviors efficiently. Traditional IM methods prioritize overall influence spread, neglecting fairness considerations regarding who benefits from that spread. This oversight might result in particular disadvantaged demographic groups being omitted from seed selection and thus receiving critical information later, or not at all. This raises ethical concerns in scenarios involving health, education, or employment resources. Fair Influence Maximization (FIM) seeks to ensure equitable diffusion across predefined groups. While early FIM approaches, such as max–min fairness, address disparities in a simple way, they fail to capture subtler inequities. Recent frameworks incorporate statistical measures such as the Gini coefficient to evaluate inequality across influenced nodes. This work introduces FIMMOGA++, an enhanced genetic algorithm that integrates multi-objective optimization and fairness-aware design. It combines traditional IM heuristics with Reverse Influence Sampling (RIS) and the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to simultaneously optimize influence spread and multiple fairness criteria. By producing a Pareto-optimal set of solutions, FIMMOGA++ provides balanced strategies that advance both theoretical understanding and practical implementation of fair influence maximization in large-scale networks.

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