Parallel PageRank Computation with AI-Driven Fairness Analysis: A Web-Based Framework

Abstract

PageRank remains a cornerstone algorithm for ranking nodes in large-scale networks, yet modern challenges demand improvements in both scalability and fairness. Traditional implementations are computationally expensive for billions of nodes and often exhibit biases, favoring highly connected entities while suppressing emerging or underrepresented nodes. This paper introduces a novel framework that integrates parallel PageRank computation with an AI-driven fairness analysis module, deployed as a professional web application. The frame- work employs MPI, OpenMP, and CUDA for distributed and accelerated execution, while machine learning models evaluate ranking bias through fairness metrics such as exposure parity and diversity. Experimental results demonstrate significant reductions in execution time and the ability to highlight imbalances in rankings. The system further provides actionable recommendations to mitigate bias, ensuring both efficiency and ethical outcomes. This unique combination of high-performance computing and AI fairness analysis offers a scalable and socially responsible approach to graph-based ranking systems.