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GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks

2020
Graph neural networks (GNNs) have attracted increasing interests in recent years. Due to the poor data locality and huge data movement during GNN inference, it is challenging to employ GNN to process large-scale graphs. Fortunately, processing-in-memory (PIM) architecture has been widely investigated as a promising approach to address the “Memory Wall”.
Zhao Wang   +6 more
openaire   +1 more source

GNN-EA: Graph Neural Network with Evolutionary Algorithm

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2022
Yun Huang, Chaobo Zhang, Junli Wang 0001
openaire   +1 more source

Fp-Gnn: Adaptive Fpga Accelerator for Graph Neural Networks

SSRN Electronic Journal, 2022
Teng Tian   +5 more
openaire   +1 more source

CC-GNN: A Community and Contraction-based Graph Neural Network

2022 IEEE International Conference on Data Mining (ICDM), 2022
Zhiyuan Li   +3 more
openaire   +1 more source

AIC-GNN: Adversarial information completion for graph neural networks

Information Sciences, 2023
Quanmin Wei   +4 more
openaire   +1 more source

Genetic-GNN: Evolutionary architecture search for Graph Neural Networks

Knowledge-Based Systems, 2022
Min Shi 0001   +6 more
openaire   +1 more source

Multiphysical graph neural network (MP-GNN) for COVID-19 drug design

Briefings in Bioinformatics, 2022
Xiang Liu, Kelin Xia
exaly  

LR-GNN: a graph neural network based on link representation for predicting molecular associations

Briefings in Bioinformatics, 2022
Han Zhang, Shenwei Huang, Yanbin Yin
exaly  

HITS-GNN: A Simplified Propagation Scheme for Graph Neural Networks

2022 IEEE International Conference on Big Data (Big Data), 2022
Mehak Khan   +4 more
openaire   +1 more source

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