116 Open source projects that are alternatives of or similar to LPGNN

Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022

Graph-based machine learning for chemical property prediction

Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch

Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

Graph Neural Networks with Keras and Tensorflow 2.

A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which appeared in The International Workshop on Self-Supervised Learning for the Web (SSL'21) @ the Web Conference 2021 (WWW'21).

AAAI 2020 - ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations

Demo of RouteNet in ACM SIGCOMM'19

Disentagnled Graph Collaborative Filtering, SIGIR2020

A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).

Summary of social recommendation papers and codes

The official code and datasets for "Zero-Shot Multi-View Indoor Localization via Graph Location Networks" (ACMMM 2020)

Code for the CHAMPS Predicting Molecular Properties Kaggle competition

Federated Principal Component Analysis Revisited!

Visualization tool for Graph Neural Networks

AAAI'21: Data Augmentation for Graph Neural Networks

Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".

odr.chalmers.se/handle/20.500.12380/256629?locale=en

A spherical CNN for weather forecasting

Google's differential privacy libraries.

Context-Aware Visual Compatibility Prediction (https://arxiv.org/abs/1902.03646)

Naive implementation of basic Differential-Privacy framework and algorithms

Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond

source code for signed graph attention networks (ICANN2019) & SDGNN (AAAI2021)

Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"

The origin of the Non-IID phenomenon is the personalization of users, who generate the Non-IID data. With Non-IID (Not Independent and Identically Distributed) issues existing in the federated learning setting, a myriad of approaches has been proposed to crack this hard nut. In contrast, the personalized federated learning may take the advantage…

Code and resources on scalable and efficient Graph Neural Networks

Code snippets and solutions for the Introduction to Deep Learning and Neural Networks Course hosted in educative.io

GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

An index of recommendation algorithms that are based on Graph Neural Networks.

Material for the EPFL master course "A Network Tour of Data Science", edition 2019.

Code of the NeurIPS 2021 paper: Language and Visual Entity Relationship Graph for Agent Navigation

Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph Machine Learning.

Tutorials for Machine Learning on Graphs

Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)

Subgraph Neural Networks (NeurIPS 2020)

Awesome Graph Self-Supervised Learning

The core library of differential privacy algorithms powering the OpenDP Project.

Bachelor's Thesis in Computer Science: Privacy-Preserving Federated Learning Applied to Decentralized Data

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach, AAAI2020

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (https://arxiv.org/abs/1610.05755)

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

Fit interpretable models. Explain blackbox machine learning.

Pytorch differentiable molecular dynamics

Privacy-Preserving Data Analysis using Pandas

PyTorch code for CVIU paper "AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction"

A toolbox for differentially private data generation

[ICLR 2021] How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision

Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning (authors' MXNet implementation for the TACL19 paper)

Tools and service for differentially private processing of tabular and relational data

A machine-learning-based tool for discovering differential privacy violations in black-box algorithms.

Easy differential privacy in R

Code and data for ECML-PKDD paper "Social Influence Attentive Neural Network for Friend-Enhanced Recommendation"

Healthcare-Researcher-Connector Package: Federated Learning tool for bridging the gap between Healthcare providers and researchers

A distributed graph deep learning framework.

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

🔨 🍇 💻 🚀 GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba 来自阿里巴巴的一站式大规模图计算系统 图分析 图查询 图机器学习

Official code for "InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization" (ICLR 2020, spotlight)