Abstract—We propose a deep learning solution to the inverse problem of localizing sources of network diffusion. Invoking.
Oct 18, 2022 · Abstract: We propose a deep learning solution to the inverse problem of localizing sources of network diffusion.
Aug 30, 2022 · Learning to Identify Sources of Network Diffusion. EUSIPCO 2022. 1. Page 2. Network Science analytics. Clean energy and grid analy,cs. Online ...
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Aug 29, 2022 · We propose a deep learning solution to the inverse problem of localizing sources of network diffusion. Invoking graph signal processing ...
This paper proposes a tractable model of Bayesian learning on large random networks where agents choose whether to adopt an innovation. We study the impact of ...
Specifically, we first present a reverse propagation method to detect recovered and unobserved infected nodes in the network, and then we use community cluster ...
Aug 31, 2024 · Our framework for solving the source identification prob- lem consists of two main stages: it first learns a continuous- time diffusion network ...
NBDA fits agent-based models of social and asocial learning to the observed data using maximum-likelihood estimation. The underlying learning mechanism can then ...
Missing: Sources | Show results with:Sources
We introduce a statistical framework for the study of diffusion source identification and develop a confidence set inference approach inspired by hypothesis ...
We then design an efficient single source detection algorithm, which leverages these mathematical models of diffusion, and the assumption that the start time of ...