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Graph neural networks (GNNs) are typically applied to static graphs that are assumed to be known upfront.
On the shortest spanning subtree of a graph and the traveling salesman problem
Joseph B Kruskal · 1956
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An improved equivalence algorithm
Bernard A Galler and Michael J Fisher · 1964
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Algorithm for solution of a problem of maximum flow in networks with power estimation
Efim A Dinic · 1970
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Efficiency of a good but not linear set union algorithm
Robert Endre Tarjan · 1975
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An on-line edge-deletion problem
Yossi Shiloach and Shimon Even · 1981
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A data structure for dynamic trees
Daniel D Sleator and Robert Endre Tarjan · 1983
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Worst-case analysis of set union algorithms
Robert E Tarjan and Jan Van Leeuwen · 1984
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Finding biconnected componemts and computing tree functions in logarithmic parallel time
Robert Endre Tarjan and Uzi Vishkin · 1984
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The cell probe complexity of dynamic data structures
Michael Fredman and Michael Saks · 1989
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Introduction to algorithms
Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford Stein · 2009
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Acyclic orientations of graphs
Richard P Stanley · 2009
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Dynamic trees in practice
Robert E Tarjan and Renato F Werneck · 2010
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Disjoint set union with randomized linking
Ashish Goel, Sanjeev Khanna, Daniel H Larkin, and Robert E Tarjan · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Wojciech Zaremba and Ilya Sutskever · 2014
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Łukasz Kaiser and Ilya Sutskever · 2015
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Order matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 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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Neural episodic control
Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adria Puigdomenech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, and Charles Blundell · 2017
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Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Neural execution of graph algorithms
Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, and Charles Blundell · 2019
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Improving graph attention networks with large margin-based constraints
Guangtao Wang, Rex Ying, Jing Huang, and Jure Leskovec · 2019
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Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
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What can neural networks reason about?
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2019
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James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, and Skye Wanderman-Milne · 2018
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Graphite: Iterative generative modeling of graphs
Aditya Grover, Aaron Zweig, and Stefano Ermon · 2018
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Relational inductive bias for physical construction in humans and machines
Jessica B Hamrick, Kelsey R Allen, Victor Bapst, Tina Zhu, Kevin R McKee, Joshua B Tenenbaum, and Peter W Battaglia · 2018
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Neural relational inference for interacting systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 2018
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Attention, learn to solve routing problems!
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Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 2018
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Janossy pooling: Learning deep permutation-invariant functions for variable-size inputs
Ryan L Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2018
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Lingxiao Zhao and Leman Akoglu · 2019
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Can graph neural networks count substructures?
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Benchmarking graph neural networks
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Generalization and representational limits of graph neural networks
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