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Graph representation learning (GRL) methods, such as graph neural networks and graph transformer models, have been successfully used to analyze graph-structured data, mainly focusing on node classification and link prediction tasks.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad · 2008
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael Gutmann and Aapo Hyvärinen · 2012
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Defining and evaluating network communities based on ground-truth
Jaewon Yang and Jure Leskovec · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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struc2vec : Learning node representations from structural identity
Leonardo Filipe Rodrigues Ribeiro, Pedro H. P. Saverese, and Daniel R. Figueiredo · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Story embedding: Learning distributed representations of stories based on character networks (extended abstract)
O-Joun Lee and Jason J. Jung · 2020
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Learning hierarchical representations of stories by using multi-layered structures in narrative multimedia
O-Joun Lee, Jason J. Jung, and Jin-Taek Kim · 2020
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Story embedding: Learning distributed representations of stories based on character networks
O-Joun Lee and Jason J. Jung · 2020
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Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Simple and deep graph convolutional networks
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li · 2020
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Deepergcn: All you need to train deeper gcns
Guohao Li, Chenxin Xiong, Ali K. Thabet, and Bernard Ghanem · 2020
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A generalization of transformer networks to graphs
Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns
Susheel Suresh, Vinith Budde, Jennifer Neville, Pan Li, and Jianzhu Ma · 2021
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Breaking the limits of message passing graph neural networks
Muhammet Balcilar, Pierre Héroux, Benoit Gaüzère, Pascal Vasseur, Sébastien Adam, and Paul Honeine · 2021
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Day-ahead hourly solar irradiance forecasting based on multi-attributed spatio-temporal graph convolutional network
Hyeon-Ju Jeon, Min-Woo Choi, and O-Joun Lee · 2022
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How attentive are graph attention networks?
Shaked Brody, Uri Alon, and Eran Yahav · 2022
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Structure-aware transformer for graph representation learning
Dexiong Chen, Leslie O’Bray, and Karsten M. Borgwardt · 2022
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Vijay Prakash Dwivedi and Xavier Bresson · 2021
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Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
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Learning contextual representations of citations via graph transformer
Hyeon-Ju Jeon, Gyu-Sik Choi, Se-Young Cho, Hanbin Lee, Hee Yeon Ko, Jason J Jung, O-Joun Lee, and Myeong-Yeon Yi · 2021
Cited alongside, same era.
Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
Cited alongside, same era.
Interinstitutional research team formation based on bibliographic network embedding
O-Joun Lee, Seungha Hong, and Jin-Taek Kim · 2021
Cited alongside, same era.
Learning multi-resolution representations of research patterns in bibliographic networks
O-Joun Lee, Hyeon-Ju Jeon, and Jason J. Jung · 2021
Cited alongside, same era.
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2022
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Recipe for a general, powerful, scalable graph transformer
Ladislav Rampásek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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Hierarchical graph transformer with adaptive node sampling
Zaixi Zhang, Qi Liu, Qingyong Hu, and Chee-Kong Lee · 2022
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Graph representation learning and its applications: A survey
Van Thuy Hoang , Hyeon-Ju Jeon , Eun-Soon You , Yoewon Yoon , Sungyeop Jung , and O-Joun Lee · 2023
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Connector 0.5: A unified framework for graph representation learning
Thanh Sang Nguyen, Jooho Lee, Van Thuy Hoang, O Lee, et al · 2023
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Graph clustering with graph neural networks
Anton Tsitsulin, John Palowitch, Bryan Perozzi, and Emmanuel Müller · 2023
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