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Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points.
Learning with local and global consistency
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Schölkopf · 2004
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Regularization on discrete spaces
Dengyong Zhou and Bernhard Schölkopf · 2005
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad · 2008
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Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites
Dario Ghersi and Roberto Sanchez · 2009
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Social influence analysis in large-scale networks
Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang · 2009
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Understanding and predicting druggability. a high-throughput method for detection of drug binding sites
Peter Schmidtke and Xavier Barril · 2010
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Novel approach for selecting the best predictor for identifying the binding sites in dna binding proteins
R Nagarajan, Shandar Ahmad, and M Michael Gromiha · 2013
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The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst · 2013
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Inferring networks of substitutable and complementary products
Julian J. McAuley, Rahul Pandey, and Jure Leskovec · 2015
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How to learn a graph from smooth signals
Vassilis Kalofolias · 2016
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Snap datasets: Stanford large network dataset collection., 2016
Jure Leskovec and Andrej Krevl · 2016
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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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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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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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
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger · 2019
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Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems
Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, and Guihai Chen · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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The open catalyst 2020 (OC20) dataset and community challenges
Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon M. Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, and Zachary W. Ulissi · 2020
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
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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
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SIGN: scalable inception graph neural networks
Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael M. Bronstein, and Federico Monti · 2020
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Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models
Benedek Rozemberczki and Rik Sarkar · 2020
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Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter W. Battaglia · 2020
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Cadtransformer: Panoptic symbol spotting transformer for CAD drawings
Zhiwen Fan, Tianlong Chen, Peihao Wang, and Zhangyang Wang · 2022
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p p -Laplacian based graph neural networks
Guoji Fu, Peilin Zhao, and Yatao Bian · 2022
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Global self-attention as a replacement for graph convolution
Md. Shamim Hussain, Mohammed J. Zaki, and Dharmashankar Subramanian · 2022
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Pure transformers are powerful graph learners
Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong · 2022
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Finding global homophily in graph neural networks when meeting heterophily
Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, and Weining Qian · 2022
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Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor K. Prasanna · 2020
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Graph-bert: Only attention is needed for learning graph representations
Jiawei Zhang, Haopeng Zhang, Congying Xia, and Li Sun · 2020
Cited alongside, same era.
Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
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On joint learning for solving placement and routing in chip design
Ruoyu Cheng and Junchi Yan · 2021
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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, Jakob Uszkoreit, and Neil Houlsby · 2021
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Gnnautoscale: Scalable and expressive graph neural networks via historical embeddings
Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Jure Leskovec · 2021
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Room-and-object aware knowledge reasoning for remote embodied referring expression
Chen Gao, Jinyu Chen, Si Liu, Luting Wang, Qiong Zhang, and Qi Wu · 2021
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Graphde: A generative framework for debiased learning and out-of-distribution detection on graphs
Zenan Li, Qitian Wu, Fan Nie, and Junchi Yan · 2022
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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
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Masked transformer for neighhourhood-aware click-through rate prediction
Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Peilin Zhao, Junzhou Huang, Da Luo, Kangyi Lin, and Sophia Ananiadou · 2022
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Recipe for a general, powerful, scalable graph transformer
Ladislav Rampásek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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Handling distribution shifts on graphs: An invariance perspective
Qitian Wu, Hengrui Zhang, Junchi Yan, and David Wipf · 2022
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Nodeformer: A scalable graph structure learning transformer for node classification
Qitian Wu, Wentao Zhao, Zenan Li, David Wipf, and Junchi Yan · 2022
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Geometric knowledge distillation: Topology compression for graph neural networks
Chenxiao Yang, Qitian Wu, and Junchi Yan · 2022
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Learning substructure invariance for out-of-distribution molecular representations
Nianzu Yang, Kaipeng Zeng, Qitian Wu, Xiaosong Jia, and Junchi Yan · 2022
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Alphafold2-aware protein-dna binding site prediction using graph transformer
Qianmu Yuan, Sheng Chen, Jiahua Rao, Shuangjia Zheng, Huiying Zhao, and Yuedong Yang · 2022
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Graph-less neural networks: Teaching old mlps new tricks via distillation
Shichang Zhang, Yozen Liu, Yizhou Sun, and Neil Shah · 2022
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Mlpinit: Embarrassingly simple gnn training acceleration with mlp initialization
Xiaotian Han, Tong Zhao, Yozen Liu, Xia Hu, and Neil Shah · 2023
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Hardsatgen: Understanding the difficulty of hard sat formula generation and a strong structure-hardness-aware baseline
Yang Li, Xinyan Chen, Wenxuan Guo, Xijun Li, Wanqian Luo, Junhua Huang, Hui-Ling Zhen, Mingxuan Yuan, and Junchi Yan · 2023
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From distribution learning in training to gradient search in testing for combinatorial optimization
Yang Li, Jinpei Guo, Runzhong Wang, and Junchi Yan · 2023
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Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
Yi-Lun Liao and Tess E. Smidt · 2023
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A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, and Liudmila Prokhorenkova · 2023
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Energy-based out-of-distribution detection for graph neural networks
Qitian Wu, Yiting Chen, Chenxiao Yang, and Junchi Yan · 2023
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DIFFormer: Scalable (graph) transformers induced by energy constrained diffusion
Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, and Junchi Yan · 2023
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Graph neural networks are inherently good generalizers: Insights by bridging gnns and mlps
Chenxiao Yang, Qitian Wu, Jiahua Wang, and Junchi Yan · 2023
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