Simplifying approach to node classification in Graph Neural Networks-Reference-Cited by-同舟云学术

Simplifying approach to node classification in Graph Neural Networks

Author:

Maurya Sunil Kumar,Liu Xin,Murata Tsuyoshi

Funder

New Energy and Industrial Technology Development Organization

Japan Science and Technology Agency

Core Research for Evolutional Science and Technology

Japan Society for the Promotion of Science

Publisher

Elsevier BV

Subject

Modeling and Simulation,General Computer Science,Theoretical Computer Science

Reference52 articles.

1. Semi-supervised classification with graph convolutional networks;Kipf;ICLR,2017

2. Graph attention networks;Velickovic;ICLR,2018

3. MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing;Abu-El-Haija,2019

4. Simple and deep graph convolutional networks;Chen;ICML,2020

5. Tree decomposed graph neural network;Wang;CIKM,2021

Cited by 28 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

3. Graph Mamba: Towards Learning on Graphs with State Space Models;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

4. Lying Graph Convolution: Learning to Lie for Node Classification Tasks;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30