fork123aniket / model-agnostic-graph-explainability-from-scratch

Implementation of Model-Agnostic Graph Explainability Technique from Scratch in PyTorch

Model-agnostic Graph Explainability from Scratch

This repository holds a graph explainability solution, which extends the work (GraphMask Explainer) to heterogeneous as well as homogeneous Graphs, making this functionality model-agnostic. Moreover, this implementation provides both node feature-level and edge-level attributes mask (explanation subgraph), which is a binary-valued vector. All 0 values of this mask vector represent those features (and edges) of the graph that do not affect their corresponding predictions, whereas features (and edges) associated with 1 values consider to be a lot effective in influencing their associated predictions outputted by the original Graph Neural Network (GNN) model.

Requirements

• PyTorch Geometric
• PyTorch
• numpy
• scikit-learn
• tqdm

Usage

Data

This implementation of GraphMask Explainer demonstrates explainability examples for GCN, GAT, and RGCN layer-types on Node Classification (NC), Graph Classification (GC), and Link Prediction (LP) tasks.

Layer Type	Task	Dataset
GCN	NC	Cora
GCN	GC	Enzymes
GAT	NC	Cora
GAT	GC	Enzymes
GAT	LP	Cora
RGCN	NC	AIFB
RGCN	GC	Enzymes

Training and Testing

• To see the model-agnostic explainability layer’s implementation, check graphmask_explainer.py.
• To train the GraphMask Explainer and generate explanations for any of the aforementioned tasks, run graphmask_explainer_example.py.
• All hyperparameters’ settings can be tweaked (based on requirements) by altering their corresponding values provided in both graphmask_explainer.py and graphmask_explainer_example.py files.

Results

NC Task Explanation Subgraph (AIFB Dataset)	GC Task Explanation Subgraph (Enzymes Dataset)

These figures show output subgraph in which all irrelevant edges (having 0 values in the binary-valued mask) have been colored grey, whereas all relevant edges (having 1 values in the generated binary-valued mask) have been illustrated in black color. Note that, for NC task, the output subgraph contains only those nodes that lie within the 3-hop neighborhood of the parent node with index 20 and have the same relation type as the parent node has, on the other hand, for GC task, the output subgraph demonstrates explanations of the graph with index 10 present in Enzymes dataset.