Computing The Diffusion State Distance On Graphs Via Algebraic Multigrid And Random Projections
Numerical Lin. Alg. with Applic.(2018)
摘要
In this paper, we consider efficient and robust algorithms for computing the diffusion state distance (DSD) metric on graphs developed recently. In order to efficiently compute DSD, we reformulate the problem into graph Laplacians and use unsmoothed aggregation algebraic multigrid to solve the resulting linear system of equations. To further reduce the computational cost, we approximate DSD by using random projections based on the Johnson-Lindenstrauss lemma. Numerical results for real-world protein-protein interaction networks are presented to demonstrate the efficiency and robustness of the proposed new approaches.
更多查看译文
关键词
algebraic multigrid,diffusion state distance,random projections
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络