Fetching the paper…
Reading the bibliography…
Hierarchical abstractions are a methodology for solving large-scale graph problems in various disciplines.
Weighted Graph Cuts Without Eigenvectors: A Multilevel Approach
Dhillon, I.; Guan, Y.; and Kulis, B. 2007 · 1957
Earlier work this paper cites.
A Relationship Between Arbitrary Positive Matrices and Doubly Stochastic Matrices
Sinkhorn, R. 1964 · 1964
Earlier work this paper cites.
The Use of Entropy Maximising Models, in the Theory of Trip Distribution, Mode Split and Route Split
Wilson, A. G. 1969 · 1969
Earlier work this paper cites.
An efficient heuristic procedure for partitioning graphs
Kernighan, B. W.; and Lin, S. 1970 · 1970
Earlier work this paper cites.
Algebraic Multigrid
Ruge, J. W.; and Stüben, K. 1987 · 1987
Earlier work this paper cites.
A Multi-Level Algorithm For Partitioning Graphs
Hendrickson, B.; and Leland, R. 1995 · 1995
Earlier work this paper cites.
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
Karypis, G.; and Kumar, V. 1998 · 1998
Earlier work this paper cites.
A multigrid tutorial
Briggs, W. L.; Henson, V. E.; and McCormick, S. F. 2000 · 2000
Earlier work this paper cites.
Normalized Cuts and Image Segmentation
Shi, J.; and Malik, J. 2000 · 2000
Earlier work this paper cites.
Fast multiscale clustering and manifold identification
Kushnir, D.; Galun, M.; and Brandt, A. 2006 · 2006
Earlier work this paper cites.
A tutorial on spectral clustering
Luxburg, U. 2007 · 2007
Earlier work this paper cites.
The Graph Neural Network Model
Scarselli, F.; Gori, M.; Tsoi, A. C.; Hagenbuchner, M.; and Monfardini, G. 2009 · 2009
Earlier work this paper cites.
Algebraic Distance on Graphs
Chen, J.; and Safro, I. 2011 · 2011
Earlier work this paper cites.
Relaxation-based Coarsening and Multiscale Graph Organization
Ron, D.; Safro, I.; and Brandt, A. 2011 · 2011
Earlier work this paper cites.
Weisfeiler-Lehman Graph Kernels
Shervashidze, N.; Schweitzer, P.; van Leeuwen, E. J.; Mehlhorn, K.; and Borgwardt, K. M. 2011 · 2011
Earlier work this paper cites.
Lean Algebraic Multigrid (LAMG): Fast Graph Laplacian Linear Solver
Livne, O. E.; and Brandt, A. 2012 · 2012
Earlier work this paper cites.
Spectral Networks and Locally Connected Networks on Graphs
Bruna, J.; Zaremba, W.; Szlam, A.; and LeCun, Y. 2014 · 2014
Earlier work this paper cites.
Advanced coarsening schemes for graph partitioning
Safro, I.; Sanders, P.; and Schulz, C. 2014 · 2014
Cited alongside, same era.
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Duvenaud, D.; Maclaurin, D.; Aguilera-Iparraguirre, J.; Gómez-Bombarelli, R.; Hirzel, T.; Aspuru-Guzik, A.; and Adams, R. P. 2015 · 2015
Cited alongside, same era.
Deep Convolutional Networks on Graph-Structured Data
Henaff, M.; Bruna, J.; and LeCun, Y. 2015 · 2015
Cited alongside, same era.
Order Matters: Sequence to sequence for sets
Vinyals, O.; Bengio, S.; and Kudlur, M. 2015 · 2015
Cited alongside, same era.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Defferrard, M.; Bresson, X.; and Vandergheynst, P. 2016 · 2016
Cited alongside, same era.
Benchmark Data Sets for Graph Kernels
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
Fey, M.; Lenssen, J. E.; Weichert, F.; and Müller, H. 2018 · 2018
Later among the works it cites.
Spectrally approximating large graphs with smaller graphs
Loukas, A.; and Vandergheynst, P. 2018 · 2018
Later among the works it cites.
Graph Attention Networks
Velic̆ković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Liò, P.; and Bengio, Y. 2018 · 2018
Later among the works it cites.
MoleculeNet: a benchmark for molecular machine learning
Wu, Z.; Ramsundar, B.; Feinberg, E.; Gomes, J.; Geniesse, C.; Pappu, A. S.; Leswing, K.; and Pande, V. 2018 · 2018
Later among the works it cites.
An End-to-End Deep Learning Architecture for Graph Classification
Zhang, M.; Cui, Z.; Neumann, M.; and Chen, Y. 2018 · 2018
Later among the works it cites.
A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening
Bravo-Hermsdorff, G.; and Gunderson, L. M. 2019 · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kersting, K.; Kriege, N. M.; Morris, C.; Mutzel, P.; and Neumann, M. 2016 · 2016
Cited alongside, same era.
Gated Graph Sequence Neural Networks
Li, Y.; Tarlow, D.; Brockschmidt, M.; and Zemel, R. 2016 · 2016
Cited alongside, same era.
Protein Interface Prediction using Graph Convolutional Networks
Fout, A.; Byrd, J.; Shariat, B.; and Ben-Hur, A. 2017 · 2017
Cited alongside, same era.
Neural Message Passing for Quantum Chemistry
Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; and Dahl, G. E. 2017 · 2017
Cited alongside, same era.
Inductive Representation Learning on Large Graphs
Hamilton, W. L.; Ying, R.; and Leskovec, J. 2017 · 2017
Cited alongside, same era.
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
Jin, W.; Coley, C. W.; Barzilay, R.; and Jaakkola, T. 2017 · 2017
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N.; and Welling, M. 2017 · 2017
Cited alongside, same era.
Closest in time.
Graph U-Nets
Gao, H.; and Ji, S. 2019 · 2019
Closest in time.
Solving graph compression via optimal transport
Garg, V. K.; and Jaakkola, T. 2019 · 2019
Closest in time.
Self-Attention Graph Pooling
Lee, J.; Lee, I.; and Kang, J. 2019 · 2019
Closest in time.
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
Liao, R.; Zhao, Z.; Urtasun, R.; and Zemel, R. 2019 · 2019
Closest in time.
Graph reduction with spectral and cut guarantees
Loukas, A. 2019 · 2019
Closest in time.
Provably Powerful Graph Networks
Maron, H.; Ben-Hamu, H.; Serviansky, H.; and Lipman, Y. 2019 · 2019
Closest in time.
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
Morris, C.; Ritzert, M.; Fey, M.; Hamilton, W. L.; Lenssen, J. E.; Rattan, G.; and Grohe, M. 2019 · 2019
Closest in time.
Computational Optimal Transport
Peyré, G.; and Cuturi, M. 2019 · 2019
Closest in time.
Optimal Transport for structured data with application on graphs
Vayer, T.; Chapel, L.; Flamary, R.; Tavenard, R.; and Courty, N. 2019 · 2019
Closest in time.
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
Ma, T.; Ferber, P.; Huo, S.; Chen, J.; and Katz, M. 2020 · 2020
Closest in time.