Description of feature
Given a batch of $i$ PyG objects with all different number of nodes $N_i$.
The current _spatial_node_loader randomly masks $L$ nodes, such as $L = P * min(N)$ where $P \in [0,1]$ is the percentage of nodes to be masked and $min(N)$ are the number of nodes of the smallest graph.
Problem: This approach potentially misses nodes from underrepresented categories (e.i. rare cell types) due to two reasons: 1) random selection of nodes doesn't consider the imbalanced dataset, 2) only masking a small fraction of nodes each time, especially considering that graph sizes might vary a lot.
An example dataset is provided in tests/test_geome_dataloader.py
Description of feature
Given a batch of$i$ PyG objects with all different number of nodes $N_i$ .$L$ nodes, such as $L = P * min(N)$ where $P \in [0,1]$ is the percentage of nodes to be masked and $min(N)$ are the number of nodes of the smallest graph.
The current
_spatial_node_loaderrandomly masksProblem: This approach potentially misses nodes from underrepresented categories (e.i. rare cell types) due to two reasons: 1) random selection of nodes doesn't consider the imbalanced dataset, 2) only masking a small fraction of nodes each time, especially considering that graph sizes might vary a lot.
An example dataset is provided in
tests/test_geome_dataloader.py