Optimized on-line computation of PageRank algorithm
This work addresses the computational bottleneck in PageRank for web search and network analysis, presenting an incremental improvement.
The paper tackled the problem of accelerating the computation of the PageRank eigenvector by introducing new ideas based on a fluid diffusion model and algebraic equations, showing improved computational efficiency in experiments on synthetic and real datasets.
In this paper we present new ideas to accelerate the computation of the eigenvector of the transition matrix associated to the PageRank algorithm. New ideas are based on the decomposition of the matrix-vector product that can be seen as a fluid diffusion model, associated to new algebraic equations. We show through experiments on synthetic data and on real data-sets how much this approach can improve the computation efficiency.