Efficient PageRank Computation on Large-Scale Graphs: A Fast-Track Optimization Framework

Abstract

The explosive growth of large-scale networks such as the Web, Twitter, and Wikipedia has intensified the demand for efficient node-ranking algorithms. PageRank remains one of the most influential techniques for measuring node importance, yet traditional implementations face critical bottlenecks in scalability, convergence speed, and memory efficiency when applied to billion-scale graphs. Existing optimizations, ranging from sparse matrix compression to distributed computation and approximation, offer partial solutions but fail to deliver a unified balance between accuracy and performance. This study proposes a fast-track optimization framework for PageRank computation that integrates three complementary strategies: hierarchical sparse decomposition to reduce memory overhead, parallelized convergence acceleration with residual-based scheduling to improve scalability, and adaptive approximation to minimize redundant iterations under provable error bounds. The framework is implemented on heterogeneous platforms including GPUs and distributed clusters. Extensive experiments on WebGraph, Twitter, and Wikipedia demonstrate that the method reduces runtime by up to 45% and memory consumption by nearly 30% compared with state-of-the-art baselines, while maintaining Kendall's Tau accuracy above 0.96. Visualization confirms interpretability, and robustness tests validate stability under graph perturbations and dynamic updates. These results establish the framework as a scalable and reliable solution for real-time network analytics, search engines, and recommendation systems.