Graph processes exhibit a temporal structure determined by the sequence index and and a
spatial structure determined by the graph support. To learn from graph processes, an …

Modern data analytics applications on graphs often operate on domains where graph
topology is not known a priori, and hence its determination becomes part of the problem …

Graph neural networks (GNNs) are naturally distributed architectures for learning
representations from network data. This renders them suitable candidates for decentralized …

Dynamical systems consisting of a set of autonomous agents face the challenge of having to
accomplish a global task, relying only on local information. While centralized controllers are …

Dynamical systems comprised of autonomous agents arise in many relevant problems such
as multi-agent robotics, smart grids, or smart cities. Controlling these systems is of …

Accurate estimation of the states of a nonlinear dynamical system is crucial for their design,
synthesis, and analysis. Particle filters are estimators constructed by simulating trajectories …

Graph signal processing (GSP) has seen rapid developments in recent years. Since its
introduction around ten years ago, we have seen numerous new ideas and practical …

Graph neural networks (GNNs) model nonlinear representations in graph data with
applications in distributed agent coordination, control, and planning among others. Current …

The linear-quadratic controller is one of the fundamental problems in control theory. The
optimal solution is a linear controller that requires access to the state of the entire system at …

The new generation of the industrial cyber-physical system (ICPS) supported by the edge
computing technology facilitates the deep integration of sensing and control. System …