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Enabling effective and efficient machine learning (ML) over large-scale graph data (e.g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 1908
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Deep graph library: Towards efficient and scalable deep learning on graphs
Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J Smola, and Zheng Zhang · 1909
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Birds of a feather: Homophily in social networks
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Rdkit: Open-source cheminformatics, 2006
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Freebase: a collaboratively created graph database for structuring human knowledge
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Pathsim: Meta path-based top-k similarity search in heterogeneous information networks
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The anatomy of the facebook social graph
Johan Ugander, Brian Karrer, Lars Backstrom, and Cameron Marlow · 2011
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K. Burke · 2012
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Introducing the knowledge graph: things, not strings
Amit Singhal · 2012
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
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Distant supervision for relation extraction with an incomplete knowledge base
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Recent progress in organic photovoltaics: Device architecture and optical design
Weiran Cao and Jiangeng Xue · 2014
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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
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Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch · 2014
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Molecular docking and structure-based drug design strategies
Leonardo G. Ferreira, Ricardo N. Dos Santos, Glaucius Oliva, and Adriano D. Andricopulo · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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the PubChemQC Project: A Large Chemical Database from the First Principle Calculations
Maho Nakata · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Jure Leskovec and Rok Sosič · 2016
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Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 2016
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov · 2016
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A century of science: Globalization of scientific collaborations, citations, and innovations
Yuxiao Dong, Hao Ma, Zhihong Shen, and Kuansan Wang · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
William L Hamilton, Rex 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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Pubchemqc project: A large-scale first-principles electronic structure database for data-driven chemistry
Maho Nakata and Tomomi Shimazaki · 2017
Array programming with numpy
Charles R Harris, K Jarrod Millman, Stéfan J van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J Smith, et al · 2020
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Combining label propagation and simple models out-performs graph neural networks
Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, and Austin R Benson · 2020
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Johannes Klicpera, Shankari Giri, Johannes T. Margraf, and Stephan Günnemann · 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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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
Cited alongside, same era.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Fastgcn: fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
Cited alongside, same era.
Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael Bronstein, and Federico Monti · 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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Masked label prediction: Unified message passing model for semi-supervised classification
Yunsheng Shi, Zhengjie Huang, Wenjin Wang, Hui Zhong, Shikun Feng, and Yu Sun · 2020
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Mpnet: Masked and permuted pre-training for language understanding
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu · 2020
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Recent developments in the pyscf program package
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Microsoft academic graph: When experts are not enough
Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, and Anshul Kanakia · 2020
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GraphSaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2020
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Dgl-ke: Training knowledge graph embeddings at scale
Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, and George Karypis · 2020
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Large-scale graph representation learning with very deep gnns and self-supervision
Ravichandra Addanki, Peter W Battaglia, David Budden, Andreea Deac, Jonathan Godwin, Thomas Keck, Wai Lok Sibon Li, Alvaro Sanchez-Gonzalez, Jacklynn Stott, Shantanu Thakoor, et al · 2021
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Technical report of team graphmiracles in the wikikg90m-lsc track of ogb-lsc@ kdd cup 2021
Jianyu Cai, Jiajun Chen, Taoxing Pan, Zhanqiu Zhang, and Jie Wang · 2021
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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 Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, and Zachary Ulissi · 2021
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Synerise at kdd cup 2021: Node classification in massive heterogeneous graphs
Michał Daniluk, Jacek Dabrowski, Barbara Rychalska, and Konrad Gołuchowski · 2021
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Forcenet: A graph neural network for large-scale quantum calculations
Weihua Hu, Muhammed Shuaibi, Abhishek Das, Siddharth Goyal, Anuroop Sriram, Jure Leskovec, Devi Parikh, and C Lawrence Zitnick · 2021
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Technical report of wikikg90m-lsc
Weihua Peng, Donghai Bian, Yanhui Huang, Guangzhi Sheng, and Jian Sun · 2021
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Runimp: Solution for kddcup 2021 mag240m-lsc
Yunsheng Shi, PGL Team, Zhengjie Huang, Weibin Li, Weiyue Su, and Shikun Feng · 2021
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Note: Solution for kdd-cup 2021 wikikg90m-lsc
Weiyue Su, Zeyang Fang, Hui Zhong, Huijuan Wang, Siming Dai, Zhengjie Huang, Yunsheng Shi, Shikun Feng, and Zeyu Chen · 2021
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Bootstrapped representation learning on graphs
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Veličković, and Michal Valko · 2021
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Team littleant’s solution of task2
Shuo Yang, Daixin Wang, Dingyuan Zhu, Yakun Wang, and Borui Ye · 2021
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Awardee solution of kdd cup 2021 ogb large-scale challenge graph-level track
Chengxuan Ying, Mingqi Yang, Shuxin Zheng, Guolin Ke, Shengjie Luo, Tianle Cai, Chenglin Wu, Yuxin Wang, Yanming Shen, and Di He · 2021
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Litegem: Lite geometry enhanced molecular representation learning for quantum property prediction
Shanzhuo Zhang, Lihang Liu, Sheng Gao, Donglong He, Xiaomin Fang, Weibin Li, Zhengjie Huang, Weiyue Su, and Wenjin Wang · 2021
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