The topic of this special issue of IEEE Signal Processing Magazine is timely and deals with a subject matter that has been receiving immense attention from various research communities, and not only within the signal processing community. Extensive research efforts on information processing over graphs exist within other fields such as statistics, computer science, optimization, control, economics, machine learning, biological sciences, and social sciences. Different fields tend to emphasize different aspects and challenges; nevertheless, opportunities for mutual cooperation are abundantly clear, and the role that signal processing plays in this domain is of fundamental importance. This is because, in all these fields, there is growing interest in performing inference and learning over graphs, such as deducing relationships from interconnections over social networks, modeling interactions among agents in biological networks, performing resource allocation distributively, passing information over networks, optimizing utility functions over graphs, adapting and learning over graphs, etc. Commonalities and significant signal processing run across all these applications. The articles in this special issue help highlight this interplay among disciplines and the significant role that signal processing plays in this domain.

Adaptation and learning over complex networks / Ali H., Sayed; Barbarossa, Sergio; Sergios, Theodoridis; Isao, Yamada. - In: IEEE SIGNAL PROCESSING MAGAZINE. - ISSN 1053-5888. - 30:3(2013), pp. 14-15. [10.1109/msp.2013.2240191]

Adaptation and learning over complex networks

BARBAROSSA, Sergio;
2013

Abstract

The topic of this special issue of IEEE Signal Processing Magazine is timely and deals with a subject matter that has been receiving immense attention from various research communities, and not only within the signal processing community. Extensive research efforts on information processing over graphs exist within other fields such as statistics, computer science, optimization, control, economics, machine learning, biological sciences, and social sciences. Different fields tend to emphasize different aspects and challenges; nevertheless, opportunities for mutual cooperation are abundantly clear, and the role that signal processing plays in this domain is of fundamental importance. This is because, in all these fields, there is growing interest in performing inference and learning over graphs, such as deducing relationships from interconnections over social networks, modeling interactions among agents in biological networks, performing resource allocation distributively, passing information over networks, optimizing utility functions over graphs, adapting and learning over graphs, etc. Commonalities and significant signal processing run across all these applications. The articles in this special issue help highlight this interplay among disciplines and the significant role that signal processing plays in this domain.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/523217
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