Special Issue: Applications of Graph Theory
Volume 106, Issue 5
Special Issue Papers
This article focuses on the problem of learning graphs from data, in particular, to capture the nonlinear and dynamic dependencies.
This article presents methods to process data associated to graphs (graph signals) extending techniques (transforms, sampling, and others) that are used for conventional signals.
This article reviews the approaches proposed to date for building graphs to summarize brain connectivity and presents their comparative review.
This article considers scenarios where a distributed computation has to be accomplished through the cooperation of multiple physically distributed nodes, with applications in the context of sensor networks, cooperating robots, among others.
This article provides an overview of the connections of algebraic graph theory and the design and analysis of electric circuits, from integrated circuits to large distribution grids.