Open rbSparky opened 3 months ago
I will use this space to add notes regarding this problem
Core Functionality:
Dropout performs random dropping operation on the node feature matrix, while DropEdge and DropNode, as the name implies, respectively act on the adjacency matrix (edges) and nodes:
After further discussion(on Slack), since it has been decided that it would be more valuable to implement more relevant applications, this discussion thread can now be used for writing the tutorial for "Molecular Property Prediction using EGNNs". It can serve as a part 2 to the current published tutorial, adding a regression task(using QM9) since currently only classification is shown Paper
Dataset to be used: QM9
The above techniques from DropMessage can be incorporated as data augmentation technique (to show the user how to write custom functions for tasks like these)
This tutorial is in progress!
Recently I had planned to work on some experiments related to the DropMessage paper and its extensions and application on various datasets like
If it would be considered useful, I could write up a tutorial/blog related to the work to show GNN.jl users how to modify architectures and use them on various datasets and experiment along the way. Experiments include:
Let me know if this will be useful for the repository! 😄 Thanks