susan1314

building-data-genome-project-2

Whole building non-residential hourly energy meter data from the Great Energy Predictor III competition

Benchmarking_PGNN

This repositories contains the source code to carry out benchmarking of position aware graph neural networks

CaptainBlackboard

船长关于机器学习、计算机视觉和工程技术的总结和分享

CompGCN

ICLR 2020: Composition-Based Multi-Relational Graph Convolutional Networks

Deep-Generative-Network

DEMO-Net

DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification

dgcnn-1

dist_grid_identification

A simulation framework for topology identification and model parameter estimation in power distribution grids: https://ieeexplore.ieee.org/document/8601410

dyrep_torch

gated-graph-transformers

Transformers are Graph Neural Networks!

gcmc

Re-Implement "Graph Convolutional Matrix Completion" (PyTorch and PyTorch Geometric)

gcn_tutorial

A tutorial on Graph Convolutional Neural Networks

GRACE

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

graph-classification

A collection of graph classification methods

graph_nn

Graph Classification with Graph Convolutional Networks in PyTorch (NeurIPS 2018 Workshop)

GraphLoG

Implementation of Self-supervised Graph-level Representation Learning with Local and Global Structure (ICML 2021).

grid_loss_prediction

A power grid transports the electricity from power producers to the consumers. But all that is produced is not delivered to the customers. Some parts of it are lost in either transmission or distribution. In Norway, the grid companies are responsible for reporting this grid loss to the institutes responsible for national transmission networks. They have to nominate the expected loss day ahead to the market so that the electricity price can be decided. The physics of grid losses are well understood and can be calculated quite accurately given the grid configuration. Still, as these are not known or changes all the time, calculating grid losses is not straight forward.

InfoGraph

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

jodie

A PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"

Label-Contrastive-Coding-based-Graph-Neural-Network-for-Graph-Classification-

Python Implementation

mvgrl

DGL Implementation of ICML 2020 Paper 'Contrastive Multi-View Representation Learning on Graphs'

P-GNN

Position-aware Graph Neural Networks

PathPlanning

Common used path planning algorithms with animations.

pydgrid

Python Distribution Grid Simulator

RGCN-pytorch

Pytorch implementation of Relational GCN for node classification

Source-Code-Notebook

关于一些经典论文源码的逐行中文笔记

SS-GCNs

[ICML 2020] "When Does Self-Supervision Help Graph Convolutional Networks?" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen

text_gcn_tutorial

A tutorial & minimal example (8min on CPU) for Graph Convolutional Networks for Text Classification. AAAI 2019

tgn

TGN: Temporal Graph Networks

VGRNN

Variational Graph Recurrent Neural Networks - PyTorch