Datasets:

anonymousPaper5674
/

Property-Driven-GNN

license: cc-by-4.0
task_categories:
  - graph-ml
language:
  - en
pretty_name: Property-Driven GNN Benchmark
size_categories:
  - 1M<n<10M
tags:
  - graph-neural-networks
  - benchmark
  - gnn-expressiveness
  - formal-methods
  - alloy
  - graph-classification

Property-Driven GNN Benchmark

A large-scale benchmark for evaluating the expressive power of Graph Neural Networks (GNNs) across 16 fundamental graph properties. This dataset accompanies the paper "Systematic Property-Driven Evaluation of GNN Expressiveness".

Summary

The benchmark contains 352 graph-classification datasets organized into two families:

GraphRandom (176 datasets): graphs that either satisfy or randomly violate a given property.
GraphPerturb (176 datasets): each positive graph is paired with a structurally similar negative counterpart that differs by only one or two edges.

All datasets were generated using Alloy, a relational-logic specification language and SAT-based analyzer, ensuring exhaustive enumeration of positive samples and verifiable negative samples.

Quick Start

Download the entire dataset

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="anonymousPaper5674/property-driven-gnn",
    repo_type="dataset",
    local_dir="./dataset",
)

Load a single CSV file and reconstruct one graph

import pandas as pd
import numpy as np

# Each row encodes one graph
df = pd.read_csv("./dataset/GraphRandom-Train/antisymmetric.csv", header=None)

row = df.iloc[0].values
n = int(np.sqrt(len(row) - 1))   # graph size (number of nodes)
adj = row[:-1].reshape(n, n)     # n x n adjacency matrix (directed)
label = int(row[-1])             # 1 if the graph satisfies the property, else 0

print(f"Graph with {n} nodes, label = {label}")
print(adj)

Data Format

Each CSV file represents one dataset. The format is:

No header row.
Each row encodes one graph.
The first n² values are the entries of the n×n adjacency matrix, flattened in row-major order: adj[i][j] = row[i * n + j].
The last value is the binary label: 1 if the graph satisfies the property, 0 otherwise.
Edges are directed: adj[i][j] = 1 means there is an edge from node i to node j.
Self-loops are allowed (diagonal entries can be 1).
Graph size n can be inferred from the row length: n = sqrt(len(row) - 1).

Folder Structure

Property-Driven-GNN/
├── GraphRandom-Train/
│   └── <property>.csv                   
├── GraphRandom-Test/
│   └── <property>/
│       └── <property>_new<k>.csv        
├── GraphPerturb-Train/
│   └── <property>.csv                     
└── GraphPerturb-Test/
    └── <property>/
        └── <property>_new<k>.csv

Code

The reference implementation, including data loading, training, and evaluation across nine global pooling methods, is available at: https://anonymous.4open.science/r/Property-Driven-GNN/README.md