1 research outputs found
Computing Diffusion State Distance using Green's Function and Heat Kernel on Graphs
The diffusion state distance (DSD) was introduced by
Cao-Zhang-Park-Daniels-Crovella-Cowen-Hescott [{\em PLoS ONE, 2013}] to capture
functional similarity in protein-protein interaction networks. They proved the
convergence of DSD for non-bipartite graphs. In this paper, we extend the DSD
to bipartite graphs using lazy-random walks and consider the general
-version of DSD. We discovered the connection between the DSD
-distance and Green's function, which was studied by Chung and Yau [{\em
J. Combinatorial Theory (A), 2000}]. Based on that, we computed the DSD
-distance for Paths, Cycles, Hypercubes, as well as random graphs
and . We also examined the DSD distances of two biological
networks.Comment: Accepted by the 11th Workshop on Algorithms and Models for the Web
Graph (WAW2014