susan1314's repositories
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
DEMO-Net
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
dist_grid_identification
A simulation framework for topology identification and model parameter estimation in power distribution grids: https://ieeexplore.ieee.org/document/8601410
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