yuehaowang/SoGCN
SoGCN: Second-Order Graph Convolutional Networks
This project offers a way to analyze and classify complex data structures that can be represented as graphs, such as molecular structures, social networks, or image pixels. It takes your graph-structured data and applies advanced neural network models to identify patterns or make predictions. Data scientists, researchers, and machine learning engineers who work with interconnected data will find this useful for tasks like drug discovery or image recognition.
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Use this if you need to perform advanced node or graph classification and regression tasks on various graph datasets, including molecular graphs, image superpixels, or synthetic graphs.
Not ideal if you're not comfortable working with command-line scripts and setting up deep learning environments with specific PyTorch and CUDA versions.
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Last pushed
Dec 18, 2021
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