lunaticbg/gnn_logic_global_expl

Official repository of GLGExplainer (ICLR2023)

Global Explainability of GNNs via Logic Combination of Learned Concepts

This repo is the official implementation of GLGExplainer accepted at ICLR2023.

To cite our work:

@article{azzolin2022global,
  title={Global explainability of gnns via logic combination of learned concepts},
  author={Azzolin, Steve and Longa, Antonio and Barbiero, Pietro and Liò, Pietro and Passerini, Andrea},
  journal={arXiv preprint arXiv:2210.07147},
  year={2022}
}

Using GLGExplainer

Requirements

To install the required packages, run:

conda env create -f env.yml

The torch-explain package is also needed.

⚠️ The updated version of torch-explain with the code to support GLGExplainer is not yet available. Follow the PR here.

pip install torch-explain

Extracting local explanations

To extract local explanations you can use any available Local Explainer which meets the requirements described in our paper. For a list of most commonly used Local Explainers for GNNs you can check out this survey.

Local explanations can be saved in a sub-folder similarly as done in local_explanations\PGExplainer\ and can be read by adding a custom function in code\local_explanations.py to properly read the explanations.

Running GLGExplainer

You can either train a new instance of GLGExplainer, or run the pre-trained model, via the notebooks in the code\ folder.

A compact summary of how to train GLGExplainer is the following:

import utils
import models
from local_explanations import *

# read local explanations
train_data = read_bamultishapes(split="TRAIN")
val_data   = read_bamultishapes(split="VAL")
test_data  = read_bamultishapes(split="TEST")

# group local explanations into a Dataset
dataset_train = utils.LocalExplanationsDataset(train_data, ...)
dataset_val   = utils.LocalExplanationsDataset(val_data, ...)
dataset_test  = utils.LocalExplanationsDataset(test_data, ...)

# build a PyG DataLoader
train_group_loader = utils.build_dataloader(dataset_train, ...)
val_group_loader   = utils.build_dataloader(dataset_val, ...)
test_group_loader  = utils.build_dataloader(dataset_test, ...)

# init the LEN
len_model    = models.LEN(hyper_params["num_prototypes"], 
                          hyper_params["LEN_temperature"], 
                          remove_attention=hyper_params["remove_attention"])

# init the Local Explanations Embedder
le_model     = models.LEEmbedder(num_features=hyper_params ["num_le_features"], 
                                 activation=hyper_params["activation"], 
                                 num_hidden=hyper_params["dim_prototypes"])

# init GLGExplainer
expl         = models.GLGExplainer(len_model, 
                                   le_model, 
                                   hyper_params=hyper_params,
                                   ...)

# train GLGExplainer
expl.iterate(train_group_loader, val_group_loader, plot=True)

# inspect embedding, prototypes, and logic formulas
expl.inspect(test_group_loader)