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awesome-efficient-gnn

Efficient Graph Neural Networks - a curated list of papers and projects

bnnetsimulator

BNNetSimulator is a packet-level network simulator to generate datasets for research and analysis.

ceons

Complex Elastic Optical Networks Simulator (CEONS)

datanetapi

deeprmsa

For more details, see paper: DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks

demo-routenet

Demo of RouteNet in ACM SIGCOMM'19

dgn

Pytorch implementation of differentiable group normalization (NeurIPS 2020)

dl-traff-graph

[CIKM 2021 Resource Paper] DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction (Graph Part)

dtcr

The paper "Learning Representations for Time Series Clustering"

flops-counter.pytorch

Flops counter for convolutional networks in pytorch framework

gcn

Implementation of Graph Convolutional Networks in TensorFlow

gcnp

gman

GMAN: A Graph Multi-Attention Network for Traffic Prediction (GMAN, https://fanxlxmu.github.io/publication/aaai2020/) was accepted by AAAI-2020.

gnnetworkingchallenge

RouteNet baseline for the Graph Neural Networking Challenge (https://bnn.upc.edu/challenge/)

gnnpapers

Must-read papers on graph neural networks (GNN)

gnnpaperscommnets

graph-pooling-papers

Papers on Graph Pooling (GNN-Pooling)

graph-u-nets

Pytorch implementation of Graph U-Nets (ICML19)

graph-wavenet

graph wavenet

hgcn

itu-ml5g-ps-001-parana

keras

Deep Learning for humans

literatures-on-gnn-acceleration

A reading list for deep graph learning acceleration.

ml-youtube-courses

📺 A place to discover the latest machine learning courses on YouTube.

neural_tree

oopt-gnpy

Optical Route Planning Library, Based on a Gaussian Noise Model

planetoid

Semi-supervised learning with graph embeddings

pnc

Official repository for the paper "Partition and Code: learning how to compress graphs" (NeurIPS'21) https://arxiv.org/abs/2107.01952

pygcn

Graph Convolutional Networks in PyTorch

pytorch-opcounter

Count the MACs / FLOPs of your PyTorch model.