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Reducing a graph while preserving its overall properties is an important problem with many applications.
On the random-cluster model: I. introduction and relation to other models
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Optimal coarsening of unstructured meshes
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Statistical physics of spin glasses and information processing: an introduction
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Information theory, inference and learning algorithms
David JC MacKay · 2003
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Classical coloring of graphs
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Laplacian mesh processing
Olga Sorkine · 2005
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Hierarchy in Natural and Social Sciences , volume 3 of Methodos Series
Denise Pumain, editor · 2006
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Aric Hagberg, Pieter J Swart, and Daniel A Schult · 2008
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Discrete Laplace–Beltrami operators for shape analysis and segmentation
Martin Reuter, Silvia Biasotti, Daniela Giorgi, Giuseppe Patanè, and Michela Spagnuolo · 2009
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A benchmark for 3d mesh segmentation
Xiaobai Chen, Aleksey Golovinskiy, and Thomas Funkhouser · 2009
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Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman · 2009
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Sparse representation for computer vision and pattern recognition
John Wright, Yi Ma, Julien Mairal, Guillermo Sapiro, Thomas S Huang, and Shuicheng Yan · 2010
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Mesh segmentations
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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The university of florida sparse matrix collection
Timothy A. Davis and Yifan Hu · 2011
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Spectral sparsification of graphs
Daniel A Spielman and Shang-Hua Teng · 2011
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Multi-grid methods and applications , volume 4
Wolfgang Hackbusch · 2013
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Scale-space theory in computer vision , volume 256
Tony Lindeberg · 2013
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Course of theoretical physics
Lev Davidovich Landau and Evgenii Mikhailovich Lifshitz · 2013
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Spectral sparsification of graphs: theory and algorithms
Joshua Batson, Daniel A Spielman, Nikhil Srivastava, and Shang-Hua Teng · 2013
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A multiscale pyramid transform for graph signals
David I Shuman, Mohammad Javad Faraji, and Pierre Vandergheynst · 2015
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Conditional random fields as recurrent neural networks
Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, and Philip HS Torr · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Computational fluid dynamics: principles and applications
Jiri Blazek · 2015
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Graphzoom: A multi-level spectral approach for accurate and scalable graph embedding
Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, and Zhuo Feng · 2020
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Faster graph embeddings via coarsening
Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, and Chi Wang · 2020
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Pgm-explainer: probabilistic graphical model explanations for graph neural networks
Minh N. Vu and My T. Thai · 2020
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Graph coarsening with neural networks
Chen Cai, Dingkang Wang, and Yusu Wang · 2021
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How to train your energy-based models
Yang Song and Diederik P Kingma · 2021
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Pruning of convolutional neural networks using Ising energy model
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Advanced coarsening schemes for graph partitioning
Ilya Safro, Peter Sanders, and Christian Schulz · 2015
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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Dense associative memory for pattern recognition
Dmitry Krotov and John J Hopfield · 2016
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Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer · 2016
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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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DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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Hojjat Salehinejad and Shahrokh Valaee · 2021
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Unsupervised learning of graph hierarchical abstractions with differentiable coarsening and optimal transport
Tengfei Ma and Jie Chen · 2021
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Scaling up graph neural networks via graph coarsening
Zengfeng Huang, Shengzhong Zhang, Chong Xi, Tang Liu, and Min Zhou · 2021
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On explainability of graph neural networks via subgraph explorations
Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji · 2021
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Gradient estimation with discrete stein operators
Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis Titsias, and Lester Mackey · 2022
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Graphframex: Towards systematic evaluation of explainability methods for graph neural networks
Kenza Amara, Rex Ying, Zitao Zhang, Zhihao Han, Yinan Shan, Ulrik Brandes, Sebastian Schemm, and Ce Zhang · 2022
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Graph coarsening: from scientific computing to machine learning
Jie Chen, Yousef Saad, and Zechen Zhang · 2022
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Generalized spectral coarsening
Alexandros Dimitrios Keros and Kartic Subr · 2022
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e3nn: Euclidean neural networks
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Graph-based neural acceleration for nonnegative matrix factorization
Jens Sjölund and Maria Bånkestad · 2022
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Principle of relevant information for graph sparsification
Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen, and Jose C Principe · 2022
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Gstarx: Explaining graph neural networks with structure-aware cooperative games
Shichang Zhang, Yozen Liu, Neil Shah, and Yizhou Sun · 2022
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Rolandos Alexandros Potamias, Stylianos Ploumpis, and Stefanos Zafeiriou · 2022
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A survey on explainability of graph neural networks
Jaykumar Kakkad, Jaspal Jannu, Kartik Sharma, Charu Aggarwal, and Sourav Medya · 2023
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Evaluating explainability for graph neural networks
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Graph neural networks and applied linear algebra
Nicholas S Moore, Eric C Cyr, Peter Ohm, Christopher M Siefert, and Raymond S Tuminaro · 2023
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Ising-cf: A pathbreaking collaborative filtering method through efficient Ising machine learning
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Ising-traffic: Using Ising machine learning to predict traffic congestion under uncertainty
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Same: Uncovering gnn black box with structure-aware shapley-based multipiece explanations
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Neural incomplete factorization: learning preconditioners for the conjugate gradient method
Paul Häusner, Ozan Öktem, and Jens Sjölund · 2024
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A comprehensive survey on graph reduction: Sparsification, coarsening, and condensation
Mohammad Hashemi, Shengbo Gong, Juntong Ni, Wenqi Fan, B Aditya Prakash, and Wei Jin · 2024
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Extending power of nature from binary to real-valued graph learning in real world
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