gravins/Anti-SymmetricDGN
Official code repository for the papers "Anti-Symmetric DGN: a stable architecture for Deep Graph Networks" accepted at ICLR 2023; "Non-Dissipative Propagation by Anti-Symmetric Deep Graph Networks"; and "Non-Dissipative Propagation by Randomized Anti-Symmetric Deep Graph Networks"
This is a reference implementation for Deep Graph Networks (DGNs), which are used to analyze complex, interconnected data structures like social networks, molecular structures, or transportation grids. It provides a stable architecture for DGNs to ensure more reliable analysis. The output is a trained DGN model capable of making predictions or classifications on graph-structured data. This project is for researchers and practitioners in machine learning and graph theory.
No commits in the last 6 months.
Use this if you are a researcher or advanced practitioner working with graph-structured data and need a robust, stable deep learning architecture for tasks like graph property prediction or benchmarking.
Not ideal if you are looking for an out-of-the-box solution for non-graph data, or if you are not comfortable with advanced machine learning research concepts and running command-line scripts.
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Jan 02, 2025
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