Guest Editorial Inference and Learning over Networks
Vincenzo Matta, Cédric Richard, Venkatesh Saligrama, Ali H. Sayed · IEEE Transactions on Signal and Information Processing over Networks · 2016
The papers in this special section focus on inference and leanring over network systems. Networks are everywhere. They surround us at different levels and scales, whether we deal with communications networks, transportation networks, biological colonies, social networks, power grids, sensor networks, or distributed Big Data depositories. Therefore, unveiling the principles and developing mechanisms for inference and learning over networks are goals of paramount importance for various research communities, not only for the signal processing community. The successful achievement of these goals brings together expertise from various disciplines such as machine learning, computer science, optimization, control, statistics, physics, economics, biology, and social sciences. Despite the apparent diversity, there are significant commonalities and ample opportunities for interactions and collaborations. In light of these considerations, it is not difficult to appreciate the timeliness of this special issue. The articles appearing in the issue help illustrate the fundamental role that signal processing plays in the realm of network science.