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2012 IEEE 12th International Conference on Data Mining Workshops
EigenSP: A More Accurate Shortest Path Distance Estimation on LargeScale Networks
Brussels, Belgium Belgium
December 10December 10
ISBN: 9781467351645
ASCII Text  x  
Koji Maruhashi, Junichi Shigezumi, Nobuhiro Yugami, Christos Faloutsos, "EigenSP: A More Accurate Shortest Path Distance Estimation on LargeScale Networks," 2013 IEEE 13th International Conference on Data Mining Workshops, pp. 234241, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012.  
BibTex  x  
@article{ 10.1109/ICDMW.2012.110, author = {Koji Maruhashi and Junichi Shigezumi and Nobuhiro Yugami and Christos Faloutsos}, title = {EigenSP: A More Accurate Shortest Path Distance Estimation on LargeScale Networks}, journal ={2013 IEEE 13th International Conference on Data Mining Workshops}, volume = {0}, year = {2012}, isbn = {9781467351645}, pages = {234241}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.110}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  CONF JO  2013 IEEE 13th International Conference on Data Mining Workshops TI  EigenSP: A More Accurate Shortest Path Distance Estimation on LargeScale Networks SN  9781467351645 SP234 EP241 A1  Koji Maruhashi, A1  Junichi Shigezumi, A1  Nobuhiro Yugami, A1  Christos Faloutsos, PY  2012 KW  Eigenvalues and eigenfunctions KW  Error analysis KW  Approximation methods KW  Approximation algorithms KW  Equations KW  Estimation KW  eigenvalues and eigenvectors KW  shortest path distance KW  large scale network VL  0 JA  2013 IEEE 13th International Conference on Data Mining Workshops ER   
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 realworld largescale networks, because of their infeasible time complexities. The landmarkbased methods approximate distances by using a few nodes as landmarks, and can accurately estimate shortestpath 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 shortestpath 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 landmarkbased methods on large graphs with many short distances. Empirical results suggest that EigenSP estimates small distances better than the landmarkbased methods.
Index Terms:
Eigenvalues and eigenfunctions,Error analysis,Approximation methods,Approximation algorithms,Equations,Estimation,eigenvalues and eigenvectors,shortest path distance,large scale network
Citation:
Koji Maruhashi, Junichi Shigezumi, Nobuhiro Yugami, Christos Faloutsos, "EigenSP: A More Accurate Shortest Path Distance Estimation on LargeScale Networks," icdmw, pp.234241, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
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