Learning on Graphs with Out-of-Distribution Nodes
Graph Neural Networks (GNNs) are state-of-the-art models for performing prediction tasks on graphs.
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GitHub Link
The GitHub link is https://github.com/songyyyy/kdd22-oodgat
Introduce
The repository “SongYYYY/KDD22-OODGAT” contains the implementation of “OODGAT,” a method presented in the paper “Learning on Graphs with Out-of-Distribution Nodes.” The implementation involves running scripts within a ‘pyg_data’ folder and requires dependencies like torch_geometric, networkx, sklearn, numpy, and scipy. The article provides instructions for downloading datasets, but notes changes in directory structure for recent versions of torch_geometric. The code supports testing on various datasets by modifying parameters in the ‘train.py’ script, including dataset names, splits, and hyperparameters. The hyperparameters for different datasets are listed in a table.
Graph Neural Networks (GNNs) are state-of-the-art models for performing prediction tasks on graphs.
Content
This is the repository for paper ‘Learning on Graphs with Out-of-Distribution Nodes’. To run the scripts, please create a folder named ‘pyg_data’ in the root directory. In the first run, datasets will be automatically downloaded to the folder from torch_geometric. When dataset is changed, the ‘splits’, ‘ID_classes’ and ‘continuous’ should also be changed accordingly. Hyperparameters for all datasets are listed in the following table:
Related
To balance efficiency and effectiveness, the vast majority of existing methods follow the two-pass approach, in which the first pass samples a fixed number of unobserved items by a simple static distribution and then the second pass selects the final negative items using a more sophisticated negative sampling strategy.
