In this paper we present methods for downsampling datasets defined on graphs (i.e., graph-signals) by extending downsampling results for traditional N-dimensional signals. In par...
Spectral clustering is useful for a wide-ranging set of applications in areas such as biological data analysis, image processing and data mining. However, the computational and/or...
Ling Huang, Donghui Yan, Michael I. Jordan, Nina T...
Spectral techniques have found many applications in computeraided design, including synthesis, verification, and testing. Decision diagram representations permit spectral coeffici...
Whitney J. Townsend, Mitchell A. Thornton, Rolf Dr...
Background: Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks ...
Joshua J. Forman, Paul A. Clemons, Stuart L. Schre...
Abstract—Comparing graphs to determine the level of underlying structural similarity between them is a widely encountered problem in computer science. It is particularly relevant...
Damien Fay, Hamed Haddadi, Andrew Thomason, Andrew...