GNNSafe: Out-of-Distribution Detection for Graph Data

The official implementation for ICLR23 paper "Energy-based Out-of-Distribution Detection for Graph Neural Networks"

What's news

[2023.02.09] We release the early version of our codes for reproducibility (more detailed info will be updated soon).

[2023.02.16] We provide the implementation for all baseline models used in the experiments.

      @inproceedings{wu2023gnnsafe,
      title = {Energy-based Out-of-Distribution Detection for Graph Neural Networks},
      author = {Qitian Wu and Yiting Chen and Chenxiao Yang and Junchi Yan},
      booktitle = {International Conference on Learning Representations (ICLR)},
      year = {2023}
      }

Dependence

• Ubuntu 16.04.6
• Cuda 10.2
• Pytorch 1.9.0
• Pytorch Geometric 2.0.3

More information about required packages is listed in requirements.txt.

Problem Settings

Out-of-distribution (OOD) detection on graph-structured data: Given a set of training nodes (inter-connected as a graph), one needs to train a robust classifier that can effectively identify OOD nodes (that have disparate distributions than training nodes) in the test set. In the meanwhile, the classifier should maintain decent classification performance on in-distribution testing nodes. Different from image data, OOD detection on graph data needs to handle data inter-dependence as compared below.

OOD detection often has two specific problem settings, which we introduce in the following figures in comparison with standard supervised learning.

• Supervised learning: the training and testing are based on data from the same distribution, i.e., in-distribution (IND) data. We use IND-Tr/IND-Val/IND-Te to denote the train/valid/test sets of in-distribution data.

• OOD detection w/o exposure: the training is based on pure IND-Tr, and the model is evaluated by the performance of discriminating IND-Te and out-of-distribution (OOD) data in test set (short as OOD-Te).

• OOD detection w/ OOD exposure: besides IND-Tr, the training stage is exposed to extra OOD data (short as OOD-Tr), and the model is evaluated on OOD-Te and IND-Te.

Data Splits and Protocols

For comprehensive evaluation, we introduce new benchmarks for OOD detection on graphs, with regard to distribution shifts of real-world and synthetic settings. Generally, graph datasets can be divided into single-graph and multi-graph datasets, and we follow the principles in [1] for data splits as shown below.

For five datasets in our experiments, due to different properties, the specific data splitting ways are described below. One could refer to GNNSafe/dataset.py for detailed implementation of the above data splits.

• Twitch (multi-graph dataset): This dataset contains multiple sub-graphs. We use subgraph DE as IND, subgraph EN as OODTr and subgraphs ES, FR, RU as OODTe. The IND is randomly split into IND-Tr/IND-Val/IND-Te with 1:1:8 ratio.

• Arxiv (single-graph dataset with context info): This dataset is a single graph where each node has a time label, i.e., when the paper is published. We use the time as domain information for splitting nodes into IND, OODTr and OODTe. The IND nodes are randomly split into IND-Tr/IND-Val/IND-Te with 1:1:8 ratio.

• Cora/Amazon/Coauthor (single-graph dataset w/o context info): Each of these datasets contain one single graph and no explicit domain label is given. We use the original data as IND, and follow the public splits for train/valid/test partition. As for OOD data, we modified the original dataset to obtain OODTr and OODTe, with three different ways:

  • Structure manipulation: adopt stochastic block model to randomly generate a graph for OOD data.
  • Feature interpolation: use random interpolation to create node features for OOD data.
  • Label leave-out: use nodes with partial classes as IND and leave out others for OODTr and OODTe.

Evalution Metrics: the OOD detection performance is measured by AUROC, AUPR, FPR95 for discriminating IND-Te and OOD-Te.

Key Results

Implementation Details

The folder GNNSafe/ contains all codes for our model and baselines Energy, OE, ODIN, Mahalanobis. The GNNSafe/main.py implements the pipeline for training and evaluation of these methods under our protocols.

• For our model GNNSafe and Energy, the model class is implemented in GNNSafe/gnnsafe.py and there are four versions of the model:

  • gnnsafe (GNNSafe in paper): the energy-based OOD detector with energy belief propagation

  • gnnsafe++ (GNNSafe++ in paper): the energy-based OOD detector with energy propagation and regularization loss on OOD-Tr.

  • gnnsafe w/o prop (Energy in paper): the energy-based OOD detector w/o propagation

  • gnnsafe++ w/o prop (Energy FT in paper): the energy-based OOD detector w/o propagation trained with regularization loss on OOD-Tr.

• The running scripts for training and evaluation are in GNNSafe/run.sh.

The folder GKDE&GPN contains all codes for baselines GKDE and GPN. The GKDE&GPN/main.py implements the pipeline for training and evaluation of the two methods under our protocols. The running scripts are in GKDE&GPN/run_gkde_gpn.sh.

How to run the code

1. Create a conda environment and install the required packages according to requirements.txt.

2. Create a folder ../data as data directory. The datasets we used are publicly available from Pytorch Geometric and OGB Package, and will be automatically downloaded when running our training scripts.

3. Follow the guidelines below for running the codes for different purposes.

Reproducing main results (Table 1 and 2)

We provide the commands with hyper-parameters for all datasets in GNNSafe/run.sh. For example, for Cora with structure manipulation as OOD, one can run the following scripts for training and evaluation of baseline MSP and our models.

    ### Cora with structure ood
    python main.py --method msp --backbone gcn --dataset cora --ood_type structure --mode detect --use_bn --device 1
    python main.py --method gnnsafe --backbone gcn --dataset cora --ood_type structure --mode detect --use_bn --device 1
    python main.py --method gnnsafe --backbone gcn --dataset cora --ood_type structure --mode detect --use_bn --use_reg --m_in -5 --m_out -1 --lamda 0.01 --device 1
    python main.py --method gnnsafe --backbone gcn --dataset cora --ood_type structure --mode detect --use_bn --use_prop --device 1
    python main.py --method gnnsafe --backbone gcn --dataset cora --ood_type structure --mode detect --use_bn --use_prop --use_reg --m_in -5 --m_out -1 --lamda 0.01 --device 1

Discussion results (Fig. 1, 2 and 3)

Under the GNNSafe folder, we also release the scripts for hyper-parameter searching in run_hyper_search.sh, discussion on hyper-parameter sensitivity in run_discuss.sh, and obtain results for energy visualization (Fig. 1 in paper) in run_visualize.sh. The figure plot code is provided in plot.ipynb.

More usability for our codes

1. Our pipeline also supports supervised node classification by using --mode classify. For example,

    # GCN on Cora
    python main.py --method msp --backbone gcn --dataset cora --ood_type structure --mode classify --use_bn --device 1
    python main.py --method msp --backbone mlp --dataset cora --ood_type structure --mode classify --use_bn --device 1

2. We also provide the interface for other datasets (e.g., citeseer, pubmed, ogbn-proteins, etc.) in GNNSafe/dataset.py.

3. For more OOD types, one can refer our codes in GNNSafe/dataset.py and flexibly modify the parameters or generation codes.

If you have any question when running the codes or trying to do more research based on our implementation, feel free to contact me via [email protected]

Reference

If you found our codes and introduced data splits useful, please consider citing our paper:

  @inproceedings{wu2023gnnsafe,
  title = {Energy-based Out-of-Distribution Detection for Graph Neural Networks},
  author = {Qitian Wu and Yiting Chen and Chenxiao Yang and Junchi Yan},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023}
  }

[1] Qitian Wu et al., Handling Distribution Shifts on Graphs: An Invariance Perspective. In ICLR2022.