Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
Estimating the distances of the shortest path between given pairs of nodes in a graph is a basic operation in a wide variety of applications including social network analysis, web retrieval, etc. Such applications require a response on the order of a few milliseconds, but exact algorithms to compute the distance of the shortest path exactly do not work on real-world large-scale networks, because of their infeasible time complexities. The landmark-based methods approximate distances by using a few nodes as landmarks, and can accurately estimate shortest-path distances with feasible time complexities. However, they fail at estimating small distances, as it is difficult for a few selected landmarks to cover the shortest paths of many close node pairs. To tackle this problem, we present a novel method EigenSP, that estimates the shortest-path distance by using an adjacency matrix approximated by a few eigenvalues and eigenvectors. The average relative error rate of EigenSP is lower than that of the landmark-based methods on large graphs with many short distances. Empirical results suggest that EigenSP estimates small distances better than the landmark-based methods.
Eigenvalues and eigenfunctions, Error analysis, Approximation methods, Approximation algorithms, Equations, Estimation, eigenvalues and eigenvectors, shortest path distance, large scale network
Koji Maruhashi, Junichi Shigezumi, Nobuhiro Yugami, Christos Faloutsos, "EigenSP: A More Accurate Shortest Path Distance Estimation on Large-Scale Networks", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 234-241, doi:10.1109/ICDMW.2012.110