Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) have shown impressive performance in graph representation learning, but they face challenges in capturing long-range dependencies due to their limited expressive power.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
A new model for learning in graph domains
M. Gori, G. Monfardini, and F. Scarselli · 2005
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Satpdb: a database of structurally annotated therapeutic peptides
Sandeep Singh, Kumardeep Chaudhary, Sandeep Kumar Dhanda, Sherry Bhalla, Salman Sadullah Usmani, Ankur Gautam, Abhishek Tuknait, Piyush Agrawal, Deepika Mathur, and Gajendra PS Raghava · 2016
Earlier work this paper cites.
Principal component analysis: a review and recent developments
Ian T Jolliffe and Jorge Cadima · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Deep collective inference
John Moore and Jennifer Neville · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Xavier Bresson and Thomas Laurent · 2017
Earlier work this paper cites.
Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
Earlier work this paper cites.
An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
Earlier work this paper cites.
Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
Earlier work this paper cites.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Earlier work this paper cites.
Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
Earlier work this paper cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Earlier work this paper cites.
Janossy pooling: Learning deep permutation-invariant functions for variable-size inputs
Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2019
Earlier work this paper cites.
Relational pooling for graph representations
Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2019
Earlier work this paper cites.
Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
Earlier work this paper cites.
Layer-dependent importance sampling for training deep and large graph convolutional networks
Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, and Quanquan Gu · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Earlier work this paper cites.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
Earlier work this paper cites.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Earlier work this paper cites.
Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
Earlier work this paper cites.
On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav · 2020
Earlier work this paper cites.
A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
Earlier work this paper cites.
Big bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Can graph neural networks count substructures?
Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna · 2020
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
Cited alongside, same era.
Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2020
Cited alongside, same era.
Transformer for graphs: An overview from architecture perspective
Erxue Min, Runfa Chen, Yatao Bian, Tingyang Xu, Kangfei Zhao, Wenbing Huang, Peilin Zhao, Junzhou Huang, Sophia Ananiadou, and Yu Rong · 2022
Later among the works it cites.
FlashAttention: Fast and memory-efficient exact attention with IO-awareness
Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
Later among the works it cites.
From stars to subgraphs: Uplifting any GNN with local structure awareness
Lingxiao Zhao, Wei Jin, Leman Akoglu, and Neil Shah · 2022
Later among the works it cites.
Global self-attention as a replacement for graph convolution
Md Shamim Hussain, Mohammed J Zaki, and Dharmashankar Subramanian · 2022
Later among the works it cites.
Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Principal neighbourhood aggregation for graph nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković · 2020
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.
Representing long-range context for graph neural networks with global attention
Zhanghao Wu, Paras Jain, Matthew Wright, Azalia Mirhoseini, Joseph E Gonzalez, and Ion Stoica · 2021
Cited alongside, same era.
A large-scale database for graph representation learning
Scott Freitas, Yuxiao Dong, Joshua Neil, and Duen Horng Chau · 2021
Cited alongside, same era.
Ogb-lsc: A large-scale challenge for machine learning on graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
Cited alongside, same era.
Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
Cited alongside, same era.
Mlp-mixer: An all-mlp architecture for vision
Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al · 2021
Cited alongside, same era.
What makes convolutional models great on long sequence modeling?
Yuhong Li, Tianle Cai, Yi Zhang, Deming Chen, and Debadeepta Dey · 2023
Later among the works it cites.
Hyena hierarchy: Towards larger convolutional language models
Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, and Christopher Ré · 2023
Later among the works it cites.
NAGphormer: A tokenized graph transformer for node classification in large graphs
Jinsong Chen, Kaiyuan Gao, Gaichao Li, and Kun He · 2023
Later among the works it cites.
Are more layers beneficial to graph transformers?
Haiteng Zhao, Shuming Ma, Dongdong Zhang, Zhi-Hong Deng, and Furu Wei · 2023
Later among the works it cites.
Graph inductive biases in transformers without message passing
Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, Puneet K Dokania, Mark Coates, Philip Torr, and Ser-Nam Lim · 2023
Later among the works it cites.
Exphormer: Sparse transformers for graphs
Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J Sutherland, and Ali Kemal Sinop · 2023
Later among the works it cites.
A generalization of vit/mlp-mixer to graphs
Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann LeCun, and Xavier Bresson · 2023
Later among the works it cites.
Hierarchical transformer for scalable graph learning
Wenhao Zhu, Tianyu Wen, Guojie Song, Xiaojun Ma, and Liang Wang · 2023
Later among the works it cites.
GOAT: A global transformer on large-scale graphs
Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, and Tom Goldstein · 2023
Later among the works it cites.
Relational attention: Generalizing transformers for graph-structured tasks
Cameron Diao and Ricky Loynd · 2023
Later among the works it cites.
Simplified state space layers for sequence modeling
Jimmy T.H. Smith, Andrew Warrington, and Scott Linderman · 2023
Later among the works it cites.
Simple hardware-efficient long convolutions for sequence modeling
Daniel Y Fu, Elliot L Epstein, Eric Nguyen, Armin W Thomas, Michael Zhang, Tri Dao, Atri Rudra, and Christopher Re · 2023
Later among the works it cites.
Hungry hungry hippos: Towards language modeling with state space models
Daniel Y Fu, Tri Dao, Khaled Kamal Saab, Armin W Thomas, Atri Rudra, and Christopher Re · 2023
Later among the works it cites.
Conversational agents in therapeutic interventions for neurodevelopmental disorders: A survey
Fabio Catania, Micol Spitale, and Franca Garzotto · 2023
Later among the works it cites.
Rwkv: Reinventing rnns for the transformer era
Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, et al · 2023
Later among the works it cites.
Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution
Eric Nguyen, Michael Poli, Marjan Faizi, Armin Thomas, Callum Birch-Sykes, Michael Wornow, Aman Patel, Clayton Rabideau, Stefano Massaroli, Yoshua Bengio, et al · 2023
Later among the works it cites.
Graph ordering attention networks
Michail Chatzianastasis, Johannes Lutzeyer, George Dasoulas, and Michalis Vazirgiannis · 2023
Later among the works it cites.
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
Later among the works it cites.
Where did the gap go? reassessing the long-range graph benchmark
Jan Tönshoff, Martin Ritzert, Eran Rosenbluth, and Martin Grohe · 2023
Later among the works it cites.
Graph transformers for large graphs
Vijay Prakash Dwivedi, Yozen Liu, Anh Tuan Luu, Xavier Bresson, Neil Shah, and Tong Zhao · 2023
Later among the works it cites.
MLPInit: Embarrassingly simple GNN training acceleration with MLP initialization
Xiaotian Han, Tong Zhao, Yozen Liu, Xia Hu, and Neil Shah · 2023
Later among the works it cites.
Layer-neighbor sampling — defusing neighborhood explosion in GNNs
Muhammed Fatih Balın and Ümit V. Çatalyürek · 2023
Later among the works it cites.
VCR-graphormer: A mini-batch graph transformer via virtual connections
Dongqi Fu, Zhigang Hua, Yan Xie, Jin Fang, Si Zhang, Kaan Sancak, Hao Wu, Andrey Malevich, Jingrui He, and Bo Long · 2024
Closest in time.
Graph-mamba: Towards long-range graph sequence modeling with selective state spaces
Chloe Wang, Oleksii Tsepa, Jun Ma, and Bo Wang · 2024
Closest in time.