In this repository we make available the Friendster dataset used in our paper:

Leonardo Teixeira, Brian Jalaian, Bruno Ribeiro. (2019). Are Graph Neural Networks Miscalibrated? ICML Workshop on Learning and Reasoning with Graph-Structured Representations.

If you use the data or code from this repository in your own code, please cite our paper:

@InProceedings{teixeira2019GNNmiscalibrated,
	title={Are Graph Neural Networks Miscalibrated?},
	author={Leonardo Teixeira and Brian Jalaian and Bruno Ribeiro},
	booktitle={ICML Workshop on Learning and Reasoning with Graph-Structured Representations},
	url={https://arxiv.org/abs/1905.02296},
	year={2019}
}

The Friendster dataset used in our paper is available in the folder data. We also provide the Train, Validation and Test split used in the paper, as well as a Python class to facilitate the usage with the PyTorch Geometric library.

We provide the dataset in HDF5 format and the data split as a NumPy NPY format file. We also provide a python class that is compatible with the PyTorch Geometric framework, which automatically downloads the data and split.

If you use the PyTorch Geometric library, we provide a Python class that can be used to access our Friendster dataset. It can automatically download and provide access to the Friendster graph (and the data split used in the paper) as an Dataset class from PyTorch Geometric.

The necessary libraries are:

• NumPy (numpy)
• PyTorch (torch)
• PyTorch Geometric (torch_geometric)
• HDF5 for Python (h5py)

Please, refer to their documentation for installation instructions (in particular for PyTorch and PyTorch Geometric). This code was tested with PyTorch 1.0.1, PyTorch Geometric 1.0.2, NumPy 1.15 and h5py 2.9.

Using the provided class is illustrated in the following snippet. The class takes care of downloading the data automatically.

from friendster import Friendster

# Download the dataset to the folder: './Friendster-25K'
dataset = Friendster(root="./Friendster-25K/")

# This dataset has a single graph
graph = dataset[0]

print(f"Friendster dataset: {graph.num_nodes} nodes")

# The data splits can be accessed as:
train_mask = graph.train_mask
validation_mask = graph.validation_mask
test_mask = graph.test_mask

A full example is given in the file example.py, where we run a GCN model on the Friendster dataset.

The dataset is available in the HDF5 format in the file friendster_25K.h5.

This file has the following HDF5 Datasets:

• adjacency: The adjacency matrix, with n_nodes rows. Each entry u is an array with the neighbors of u.
• features: The feature matrix, of shape (n_nodes, n_features). Each entry u has the features of node u.
• target: The target label of the ndoes, of shape n_nodes. Each entry u has the integer that represents the label of node u.
• feature_names: The names of each of the features. Has n_features entries.
• target_names: The names of each label.

Using the h5py library, the data can be loaded as:

from h5py import File

dataset = File("./friendster_25K.h5")
A = dataset["adjacency"][:]  # Adjacency list
X = dataset["features"][:]  # Feature matrix
y = dataset["target"][:]  # Node labels

The data split is available in the file friendster_25K.split.npz. This can be loaded with:

import numpy as np

data = np.load("friendster_25K.split.npz")
train_nodes = data["arr_0"][0]
validation_nodes = data["arr_0"][1]
test_nodes = data["arr_0"][2]