We present a new approach for designing external graph algorithms and use it to design simple, deterministic and randomized external algorithms for computing connected components, ...
James Abello, Adam L. Buchsbaum, Jeffery Westbrook
With the emergence of new applications, e.g., computational biology, new software engineering techniques, social networks, etc., more data is in the form of graphs. Locating occur...
We propose a manifold learning approach to fiber tract clustering using a novel similarity measure between fiber tracts constructed from dual-rooted graphs. In particular, to gene...
Andy Tsai, Carl-Fredrik Westin, Alfred O. Hero, Al...
Molecular sequence megaclassification is a technique for automated protein sequence analysis and annotation. Implementation of the method has been limited by the need to store and...
We study the topological simplification of graphs via random embeddings, leading ultimately to a reduction of the Gupta-Newman-Rabinovich-Sinclair (GNRS) L1 embedding conjecture t...