pytorch_geometric and gnn
These two tools are competitors, as they are both libraries for building Graph Neural Networks, but each is tied to a different deep learning framework: PyTorch Geometric for PyTorch and TensorFlow GNN for TensorFlow, meaning a user would choose one or the other based on their preferred underlying framework.
About pytorch_geometric
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
This tool helps machine learning engineers and researchers build and train Graph Neural Networks (GNNs) for analyzing structured data. It takes graph-structured data (like social networks, molecular structures, or citation graphs) as input and produces trained GNN models capable of tasks such as classifying nodes, predicting links, or generating new graphs. It is designed for those already familiar with PyTorch.
About gnn
tensorflow/gnn
TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.
This library helps machine learning engineers and researchers build and train Graph Neural Networks (GNNs) using TensorFlow. You can input complex graph data, including graphs with different types of nodes and edges, and output trained GNN models capable of tasks like molecular classification or shortest path prediction. It's designed for those working with structured data that can be represented as graphs.
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