Scalable Graph Neural Network

Scalable Graph Neural Networks (GNNs) aim to overcome the computational limitations of standard GNNs when applied to massive datasets, enabling efficient analysis of large graphs found in various domains. Current research focuses on improving the efficiency and accuracy of GNNs through techniques like graph coarsening, adaptive node propagation, and novel architecture designs that prioritize scalability while maintaining predictive performance. These advancements are crucial for tackling real-world problems involving large-scale graph data, such as social networks, knowledge graphs, and biological networks, leading to improved performance in applications like node classification and link prediction. The development of more efficient and accurate scalable GNNs is driving progress in numerous fields.

Papers

February 9, 2024

Rethinking Node-wise Propagation for Large-scale Graph Learning
Xunkai Li, Jingyuan Ma, Zhengyu Wu, Daohan Su, Wentao Zhang, Rong-Hua Li, Guoren Wang
Large Scale Graph Graph Propagation Adaptive Propagation Scalable Graph Neural Network

December 24, 2023

Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training
Charles Dickens, Eddie Huang, Aishwarya Reganti, Jiong Zhu, Karthik Subbian, Danai Koutra
Graph Convolution Graph Coarsening Graph Summarization Scalable Graph Neural Network Node Classification Datasets

October 19, 2023

MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale
Haitian Jiang, Renjie Liu, Xiao Yan, Zhenkun Cai, Minjie Wang, David Wipf
Graph Neural Network Visual Analogue Scale Node Embeddings Interpretable Way Scalable Graph Neural Network

October 17, 2023

Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation
Xinyi Gao, Wentao Zhang, Junliang Yu, Yingxia Shao, Quoc Viet Hung Nguyen, Bin Cui, Hongzhi Yin
Graph Inference Inference Acceleration Adaptive Propagation Scalable Graph Neural Network Graph Neural Network Inference

November 1, 2022

Efficient Graph Neural Network Inference at Large Scale
Xinyi Gao, Wentao Zhang, Yingxia Shao, Quoc Viet Hung Nguyen, Bin Cui, Hongzhi Yin
Large Scale Large Scale Graph Graph Inference Inference Speedup Scalable Graph Neural Network Graph Neural Network Inference Personalized Propagation

October 14, 2022

A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
Keyu Duan, Zirui Liu, Peihao Wang, Wenqing Zheng, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang
Graph Structure Benchmark Platform Comprehensive Study Large Scale Graph Scalable Graph Neural Network Scalable GNN

June 9, 2022

Graph Attention Multi-Layer Perceptron
Wentao Zhang, Ziqi Yin, Zeang Sheng, Yang Li, Wen Ouyang, Xiaosen Li, Yangyu Tao, Zhi Yang, Bin Cui
Multi Layer Graph Attention Large Scale Graph GNN Architecture Open Graph Benchmark Scalable Graph Neural Network

March 1, 2022

PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm
Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, Bin Cui
Graph Neural Network Scalable Approach Graph Neural Architecture Search Scalable Graph Neural Network Scalable GNN Specific GNN

November 30, 2021

Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks
Carter Knutson, Mridula Bontha, Jenna A. Bilbrey, Neeraj Kumar
Graph Neural Network Protein Ligand Novel Ligand Molecular Interaction Scalable Graph Neural Network