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As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings).
Reducing the bandwidth of sparse symmetric matrices
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Graph partitioning models for parallel computing
Bruce Hendrickson and Tamara G Kolda · 2000
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Link prediction approach to collaborative filtering
Hsinchun Chen, Xin Li, and Zan Huang · 2005
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Near linear time algorithm to detect community structures in large-scale networks
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Benchmark graphs for testing community detection algorithms
Andrea Lancichinetti, Santo Fortunato, and Filippo Radicchi · 2008
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MeTis: Unstructured Graph Partitioning and Sparse Matrix Ordering System, Version 4.0
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Learning spectral graph transformations for link prediction
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Non-negative laplacian embedding
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Community detection in graphs
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Alessandra Sala, Haitao Zheng, Ben Y. Zhao, Sabrina Gaito, and Gian Paolo Rossi · 2010
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Paolo Boldi, Marco Rosa, Massimo Santini, and Sebastiano Vigna · 2011
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Cauchy graph embedding
Dijun Luo, Feiping Nie, Heng Huang, and Chris H Ding · 2011
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Graph embedding in vector spaces by node attribute statistics
Jaume Gibert, Ernest Valveny, and Horst Bunke · 2012
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Powergraph: Distributed graph-parallel computation on natural graphs
Joseph E Gonzalez, Yucheng Low, Haijie Gu, Danny Bickson, and Carlos Guestrin · 2012
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Graphchi: Large-scale graph computation on just a pc
Aapo Kyrola, Guy Blelloch, and Carlos Guestrin · 2012
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Translating embeddings for modeling multi-relational data
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Spectral methods for community detection and graph partitioning
Mark EJ Newman · 2013
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X-stream: Edge-centric graph processing using streaming partitions
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An experimental comparison of pregel-like graph processing systems
Minyang Han, Khuzaima Daudjee, Khaled Ammar, M Tamer Özsu, Xingfang Wang, and Tianqi Jin · 2014
Gunrock: A high-performance graph processing library on the gpu
Yangzihao Wang, Andrew Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel, and John D Owens · 2016
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Learning graph representations with embedding propagation
Alberto Garcia Duran and Mathias Niepert · 2017
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Inductive representation learning on large graphs
Will 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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When is graph reordering an optimization? studying the effect of lightweight graph reordering across applications and input graphs
Vignesh Balaji and Brandon Lucia · 2018
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Deep feature learning via structured graph laplacian embedding for person re-identification
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Parallelization of reordering algorithms for bandwidth and wavefront reduction
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Cusha: vertex-centric graph processing on gpus
Farzad Khorasani, Keval Vora, Rajiv Gupta, and Laxmi N Bhuyan · 2014
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SNAP Datasets: Stanford large network dataset collection
Jure Leskovec and Andrej Krevl · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Enterprise: breadth-first graph traversal on gpus
Hang Liu and H Howie Huang · 2015
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De Cheng, Yihong Gong, Xiaojun Chang, Weiwei Shi, Alexander Hauptmann, and Nanning Zheng · 2018
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Tigr: Transforming irregular graphs for gpu-friendly graph processing
Amir Hossein Nodehi Sabet, Junqiao Qiu, and Zhijia Zhao · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Design principles for sparse matrix multiplication on the gpu
Carl Yang, Aydın Buluç, and John D Owens · 2018
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Hierarchical graph representation learning with differentiable pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, and Jure Leskovec · 2018
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Pytorch extension library of optimized scatter operations, 2019
Matthias Fey and Jan E. Lenssen · 2019
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Simd-x: Programming and processing of graph algorithms on gpus
Hang Liu and H Howie Huang · 2019
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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, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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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 · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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