diff --git a/tutorials/dev/.documenter-siteinfo.json b/tutorials/dev/.documenter-siteinfo.json index 806a0a918..33f90e14f 100644 --- a/tutorials/dev/.documenter-siteinfo.json +++ b/tutorials/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-11-04T12:04:05","documenter_version":"1.7.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-11-04T12:15:33","documenter_version":"1.7.0"}} \ No newline at end of file diff --git a/tutorials/dev/index.html b/tutorials/dev/index.html index 5f17e0586..993345d11 100644 --- a/tutorials/dev/index.html +++ b/tutorials/dev/index.html @@ -1,2 +1,2 @@ -Home · Tutorials
GitHub

Tutorials

Introductory tutorials

Here are some introductory tutorials to get you started:

Temporal graph neural networks tutorials

Here some tutorials on temporal graph neural networks:

Contributions

If you have a suggestion on adding new tutorials, feel free to create a new issue here. Users are invited to contribute demonstrations of their own. If you want to contribute new tutorials and looking for inspiration, checkout these tutorials from PyTorch Geometric. Please check out existing tutorials for more details.

+Home · Tutorials
GitHub

Tutorials

Introductory tutorials

Here are some introductory tutorials to get you started:

Temporal graph neural networks tutorials

Here some tutorials on temporal graph neural networks:

Contributions

If you have a suggestion on adding new tutorials, feel free to create a new issue here. Users are invited to contribute demonstrations of their own. If you want to contribute new tutorials and looking for inspiration, checkout these tutorials from PyTorch Geometric. Please check out existing tutorials for more details.

diff --git a/tutorials/dev/pluto_output/gnn_intro_pluto/index.html b/tutorials/dev/pluto_output/gnn_intro_pluto/index.html index 65634b404..7042e11a1 100644 --- a/tutorials/dev/pluto_output/gnn_intro_pluto/index.html +++ b/tutorials/dev/pluto_output/gnn_intro_pluto/index.html @@ -252,4 +252,4 @@

As one can see, our 3-layer GCN model manages to linearly separating the communities and classifying most of the nodes correctly.

Furthermore, we did this all with a few lines of code, thanks to the GraphNeuralNetworks.jl which helped us out with data handling and GNN implementations.

- + diff --git a/tutorials/dev/pluto_output/graph_classification_pluto/index.html b/tutorials/dev/pluto_output/graph_classification_pluto/index.html index 3fb4f344d..ed51d0f6c 100644 --- a/tutorials/dev/pluto_output/graph_classification_pluto/index.html +++ b/tutorials/dev/pluto_output/graph_classification_pluto/index.html @@ -207,4 +207,4 @@

Conclusion

In this chapter, you have learned how to apply GNNs to the task of graph classification. You have learned how graphs can be batched together for better GPU utilization, and how to apply readout layers for obtaining graph embeddings rather than node embeddings.

- + diff --git a/tutorials/dev/pluto_output/node_classification_pluto/index.html b/tutorials/dev/pluto_output/node_classification_pluto/index.html index fc5de7f4f..85d4373a2 100644 --- a/tutorials/dev/pluto_output/node_classification_pluto/index.html +++ b/tutorials/dev/pluto_output/node_classification_pluto/index.html @@ -304,4 +304,4 @@

Conclusion

In this tutorial, we have seen how to apply GNNs to real-world problems, and, in particular, how they can effectively be used for boosting a model's performance. In the next tutorial, we will look into how GNNs can be used for the task of graph classification.

- + diff --git a/tutorials/dev/pluto_output/temporal_graph_classification_pluto/index.html b/tutorials/dev/pluto_output/temporal_graph_classification_pluto/index.html index d3d96a1dd..7196054f2 100644 --- a/tutorials/dev/pluto_output/temporal_graph_classification_pluto/index.html +++ b/tutorials/dev/pluto_output/temporal_graph_classification_pluto/index.html @@ -186,4 +186,4 @@

TrainingConclusions

In this tutorial, we implemented a very simple architecture to classify temporal graphs in the context of gender classification using brain data. We then trained the model on the GPU for 100 epochs on the TemporalBrains dataset. The accuracy of the model is approximately 75-80%, but can be improved by fine-tuning the parameters and training on more data.

- + diff --git a/tutorials/dev/pluto_output/traffic_prediction/index.html b/tutorials/dev/pluto_output/traffic_prediction/index.html index 498fc1ae6..09dcc6ce6 100644 --- a/tutorials/dev/pluto_output/traffic_prediction/index.html +++ b/tutorials/dev/pluto_output/traffic_prediction/index.html @@ -191,4 +191,4 @@

TrainingConclusion

In this tutorial, we learned how to use a recurrent temporal graph convolutional network to predict traffic in a spatio-temporal setting. We used the TGCN model, which consists of a graph convolutional network (GCN) and a gated recurrent unit (GRU). We then trained the model for 100 epochs on a small subset of the METR-LA dataset. The accuracy of the model is not very good, but it can be improved by training on more data.

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