Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks-Reference-Cited by-同舟云学术

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Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks

Published:2022-07 Issue: Volume:159 Page:46-53
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

Thang Duong Chi^ORCID,Dat Hoang Thanh^ORCID,Tam Nguyen Thanh^ORCID,Jo Jun,Hung Nguyen Quoc Viet^ORCID,Aberer Karl

Funder

Australian Research Council

Association pour la Recherche sur le Cancer

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference53 articles.

1. Inductive representation learning on large graphs;Hamilton,2017

2. Semi-supervised classification with graph convolutional networks;Kipf,2016

3. Neural message passing for quantum chemistry;Gilmer,2017

4. Graph embeddings for one-pass processing of heterogeneous queries;Duong,2020

5. Community enhanced graph convolutional networks;Liu;Pattern Recognition Letters,2020

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1. Unsupervised Graph Representation Learning with Inductive Shallow Node Embedding;Complex & Intelligent Systems;2024-07-12

2. Swing Trend Prediction of Main Guide Bearing in Hydropower Units Based on MFS-DCGNN;Sensors;2024-05-31

3. Spammer detection on short video applications;Pattern Recognition Letters;2024-01

4. Feature Processing on Artificial Graph Node Features for Classification with Graph Neural Networks;Engineering Cyber-Physical Systems and Critical Infrastructures;2024

5. Adaptive filters in Graph Convolutional Neural Networks;Pattern Recognition;2023-12

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