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arXiv · 2007.06456

A Sampling Algorithm for Diffusion Networks

Daniel Gilio Tiglea·Renato Candido·Magno T. M. Silva
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Abstract

In this paper, we propose a sampling mechanism for adaptive diffusion networks that adaptively changes the amount of sampled nodes based on mean-squared error in the neighborhood of each node. It presents fast convergence during transient and a significant reduction in the number of sampled nodes in steady state. Besides reducing the computational cost, the proposed mechanism can also be used as a censoring technique, thus saving energy by reducing the amount of communication between nodes. We also present a theoretical analysis to obtain lower and upper bounds for the number of network nodes sampled in steady state.

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Daniel Gilio Tiglea, Renato Candido, Magno T. M. Silva. 2020-07-13. A Sampling Algorithm for Diffusion Networks. https://arxiv.org/abs/2007.06456

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