Graph neural networks is the preferred neural network architecture for processing data structured as graphs (for example, social networks or molecule structures), yielding better results than fully-connected networks or convolutional networks.
This tutorial implements a specific graph neural network known as a
Graph Attention Network (GAT)
to predict labels of scientific papers based on the papers they cite (using the
Cora dataset
).
graph-attention-nets huggingface.co is an AI model on huggingface.co that provides graph-attention-nets's model effect (), which can be used instantly with this keras-io graph-attention-nets model. huggingface.co supports a free trial of the graph-attention-nets model, and also provides paid use of the graph-attention-nets. Support call graph-attention-nets model through api, including Node.js, Python, http.
graph-attention-nets huggingface.co is an online trial and call api platform, which integrates graph-attention-nets's modeling effects, including api services, and provides a free online trial of graph-attention-nets, you can try graph-attention-nets online for free by clicking the link below.
keras-io graph-attention-nets online free url in huggingface.co:
graph-attention-nets is an open source model from GitHub that offers a free installation service, and any user can find graph-attention-nets on GitHub to install. At the same time, huggingface.co provides the effect of graph-attention-nets install, users can directly use graph-attention-nets installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
graph-attention-nets install url in huggingface.co: