Efficient Graph Neural Networks

A curated list of must-read papers on efficient Graph Neural Networks and scalable Graph Representation Learning for real-world applications.

Photo by Jake Givens

馃殌 Awesome Efficient Graph Neural Networks

This is a curated list of must-read papers on efficient Graph Neural Networks and scalable Graph Representation Learning for real-world applications.

This post is currently a placeholder. The eventual goal is to write a Lil’log style blogpost on efficient GNNs.


Efficient and Scalable GNN Architectures


Source: Simplifying Graph Convolutional Networks


Neural Architecture Search for GNNs


Source: Probabilistic Dual Network Architecture Search on Graphs


Large-scale Graphs and Sampling Techniques


Source: GraphSAINT: Graph Sampling Based Inductive Learning Method


Low Precision and Quantized GNNs


Source: Degree-Quant: Quantization-Aware Training for Graph Neural Networks


Knowledge Distillation for GNNs


Source: Distilling Knowledge from Graph Convolutional Networks


Hardware Acceleration of GNNs


Source: Computing Graph Neural Networks: A Survey from Algorithms to Accelerators


Code Frameworks, Libraries, and Datasets


Source: OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs


Industrial Applications and Systems


Source: Graph Convolutional Neural Networks for Web-Scale Recommender Systems

Chaitanya K. Joshi
Chaitanya K. Joshi
Research Engineer

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