Fetching the paper…
Reading the bibliography…
Graph neural networks (GNNs) have been shown to be highly sensitive to the choice of aggregation function.
On the Limitations of Representing Functions on Sets
Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Ingmar Posner, and Michael Osborne · 1901
Earlier work this paper cites.
What Can Neural Networks Reason About?
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 1905
Earlier work this paper cites.
Neural Execution of Graph Algorithms
Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, and Charles Blundell · 1910
Earlier work this paper cites.
The algebra of bracketings and their enumeration
Dov Tamari · 1962
Earlier work this paper cites.
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2003
Earlier work this paper cites.
Principal Neighbourhood Aggregation for Graph Nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Velickovic · 2004
Earlier work this paper cites.
DeeperGCN: All You Need to Train Deeper GCNs
Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem · 2006
Earlier work this paper cites.
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S. Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2009
Earlier work this paper cites.
Regularizing Towards Permutation Invariance in Recurrent Models
Edo Cohen-Karlik, Avichai Ben David, and Amir Globerson · 2010
Earlier work this paper cites.
Haskell 2010 language report, 2010
Simon Marlow et al · 2010
Earlier work this paper cites.
Learning Aggregation Functions
Giovanni Pellegrini, Alessandro Tibo, Paolo Frasconi, Andrea Passerini, and Manfred Jaeger · 2012
Earlier work this paper cites.
On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
Cited alongside, same era.
Neural Networks, Types, and Functional Programming, 2015
Christopher Olah · 2015
Cited alongside, same era.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R. Salakhutdinov, and Alexander J. Smola · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Can recurrent neural networks warp time?
Corentin Tallec and Yann Ollivier · 2018
Later among the works it cites.
Janossy pooling: Learning deep permutation-invariant functions for variable-size inputs
Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2019
Later among the works it cites.
Normalized attention without probability cage
Oliver Richter and Roger Wattenhofer · 2020
Later among the works it cites.
Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter Battaglia · 2020
Later among the works it cites.
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
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
Cited alongside, same era.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Cited alongside, same era.
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken Ichi Kawarabayashi, and Stefanie Jegelka · 2018
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka
Cited in the paper.
Neural execution engines: Learning to execute subroutines
Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, and Milad Hashemi · 2020
Later among the works it cites.
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
Later among the works it cites.
Kenshin Abe, Takanori Maehara, and Issei Sato · 2021
Later among the works it cites.
Neural Semirings
Pedro Zuidberg Dos Martires · 2021
Later among the works it cites.
Graph Neural Networks are Dynamic Programmers
Andrew Dudzik and Petar Veličković · 2022
Closest in time.