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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Rkein, Hassan
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Computer Science & IT Engineering

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AI-powered hub optimization: a reinforcement learning and graph-based approach to scalable blockchain networks Danach, Kassem; Rkein, Hassan; Ramadan, Alaaeddine; Harb, Hassan; Hamdar, Bassam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp536-546

Abstract

Blockchain networks face persistent scalability challenges, including network congestion, high latency, and transaction costs. To address these limitations, this study proposes an AI-driven hub location optimization framework that integrates reinforcement learning (RL), mixed integer linear programming (MILP), and graph neural networks (GNNs). The RL-based hub selection dynamically identifies optimal supernode placement, while MILP ensures cost-efficient transaction routing, and GNNs predict flow patterns for proactive congestion management. Experimental results on Ethereum and Bitcoin datasets demonstrate significant improvements, including a 58.6% reduction in transaction latency, 28.9% gas fee savings, and 41.5% congestion reduction. Beyond performance gains, statistical tests confirm the significance of these improvements, and ablation studies highlight the complementary role of each component.
Co-Authors Danach, Kassem Hamdar, Bassam Harb, Hassan Ramadan, Alaaeddine