FeiGSSS/NEDMP
Official PyTorch implementation of Neural Enhanced Dynamic Message Passing in AISTATS 2022
This project helps predict how things like diseases or information spread through a network, which is crucial for public health, marketing, or social science. It takes in information about a network's connections and initial conditions, then outputs the likelihood of each person or node being in a certain state (e.g., infected, adopted an opinion) over time. This tool is for researchers and practitioners who need accurate simulations of dynamic processes on complex networks.
No commits in the last 6 months.
Use this if you need to infer the time-dependent probabilities of states for nodes in a spreading process on complex networks, especially those with local loops where traditional methods struggle.
Not ideal if you are not comfortable running command-line scripts or if you need to simulate on operating systems other than Ubuntu, as some core components are compiled specifically for it.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Jun 23, 2022
Commits (30d)
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