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This work considers a diffusion network responding to streaming data, and studies the problem of identifying the topology of a subnetwork of observable agents by tracking their output measurements. Topology inference from indirect and/or incomplete dataset ...
The efficient design of networks has been an important engineering task that involves challenging combinatorial optimization problems. Typically, a network designer has to select among several alternatives which links to establish so that the resulting net ...
We examine the behavior of multiagent networks where information-sharing is subject to a positive communications cost over the edges linking the agents. We consider a general mean-square-error formulation, where all agents are interested in estimating the ...
This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and ...
Structural relations established among agents influence the performance of decentralized service discovery process in multiagent systems. Moreover, distributed systems should be able to adapt their structural relations to changes in environmental condition ...
The authors of a Computer article from 2002 reflect on their proposal to use networks on chips to address scalable communications on silicon VLSI chips. ...
Part I of this paper examined the mean-square stability and convergence of the learning process of distributed strategies over graphs. The results identified conditions on the network topology, utilities, and data in order to ensure stability; the results ...
Networks are data structures that are fundamental for capturing and analyzing complex interactions between objects. While they have been used for decades to solve problems in virtually all scientific fields, their usage for data analysis in real-world prac ...
In many scenarios of interest, agents may only have access to partial information about an unknown model or target vector. Each agent may be sensing only a subset of the entries of a global target vector, and the number of these entries can be different ac ...
In this paper we address the problem of quantized consensus where process noise or external inputs corrupt the state of each agent at each iteration. We propose a quantized consensus algorithm with progressive quantization, where the quantization interval ...
