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gal

[ICML 2021] Information Obfuscation of Graph Neural Networks

gca

[WWW 2021] Source code for "Graph Contrastive Learning with Adaptive Augmentation"

gcfl

This repository is the implementation of Federated Graph Classification over Non-IID Graphs.

gcnii

PyTorch implementation of "Simple and Deep Graph Convolutional Networks"

geo-defakehop

gmi-attack

gnn-benchmark

Framework for evaluating Graph Neural Network models on semi-supervised node classification task

gnn-embedding-leaks

gnn-fakenews

A collection of GNN-based fake news detection models.

gr_att_analysis

grace

[GRL+ @ ICML 2020] PyTorch implementation for "Deep Graph Contrastive Representation Learning" (https://arxiv.org/abs/2006.04131v2)

graph-adversarial-learning-literature

A curated list of adversarial attacks and defenses papers on graph-structured data.

graph_neural_networks_smai2022_g29

HOW POWERFUL ARE GRAPH NEURAL NETWORKS?

graphcl

[NeurIPS 2020] "Graph Contrastive Learning with Augmentations" by Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen

graphfl

graphframex

graphhop

gslb

A comprehensive benchmark of Graph Structure Learning (NeurIPS 2023 Datasets and Benchmarks Track)

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

ie-hgcn

Interpretable and Efficient Heterogeneous Graph Convolutional Network, IEEE TKDE 2021

improved-deep-leakage-from-gradients

The code for "Improved Deep Leakage from Gradients" (iDLG).

interpretable_cnn

This repository is deprecated, please go to https://github.com/davidsonic/Interpretable_CNNs_via_Feedforward_Design

interpretable_cnns_via_feedforward_design

Official Implementation of Interpretable Convolutional Neural Networks via Feedforward Design Arxiv: https://arxiv.org/abs/1810.02786

interpretable_cnns_via_feedforward_design-1

Official Implementation for FF_designed CNNs

interpretablecnn

jlgcn

Joing learning of graphs and features

knowledge-graph-papers

Useful research papers to help understand knowledge graph

label_propagation_weak_supervision

Code repository for the paper "Label Propagation with Weak Supervision" (ICLR 23)

lagnn

[ICML 2022] Local Augmentation for Graph Neural Networks

learning-adversarially-robust-representations

Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2002.11798)