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This report gives a summary of two problems about graph convolutional networks (GCNs): over-smoothing and heterophily challenges, and outlines future directions to explore.
Improving generalization for temporal difference learning: The successor representation
P. Dayan · 1993
Earlier work this paper cites.
Spectral graph theory
F. R. Chung and F. C. Graham · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, et al · 1998
Earlier work this paper cites.
Proto-value functions: Developmental reinforcement learning
S. Mahadevan · 2005
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
G. E. Hinton, S. Osindero, and Y.-W. Teh · 2006
Earlier work this paper cites.
Near linear time algorithm to detect community structures in large-scale networks
U. N. Raghavan, R. Albert, and S. Kumara · 2007
Earlier work this paper cites.
Dual representations for dynamic programming and reinforcement learning
T. Wang, M. Bowling, and D. Schuurmans · 2007
Earlier work this paper cites.
The block grade of a block krylov space
M. H. Gutknecht and T. Schmelzer · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko · 2013
Earlier work this paper cites.
Graph structured data viewed through a fourier lens
V. N. Ekambaram · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Deepwalk: Online learning of social representations
B. Perozzi, R. Al-Rfou, and S. Skiena · 2014
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Earlier work this paper cites.
Line: Large-scale information network embedding
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei · 2015
Earlier work this paper cites.
Geometric deep learning: Going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2016
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
Earlier work this paper cites.
node2vec: Scalable feature learning for networks
A. Grover and J. Leskovec · 2016
Earlier work this paper cites.
Reinforcement learning with unsupervised auxiliary tasks
M. Jaderberg, V. Mnih, W. M. Czarnecki, T. Schaul, J. Z. Leibo, D. Silver, and K. Kavukcuoglu · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
Earlier work this paper cites.
Deep successor reinforcement learning
T. D. Kulkarni, A. Saeedi, S. Gautam, and S. J. Gershman · 2016
Earlier work this paper cites.
Block Krylov subspace methods for functions of matrices
A. Frommer, K. Lund, and D. B. Szyld · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
Cited alongside, same era.
Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2017
Cited alongside, same era.
Geometric deep learning on graphs and manifolds using mixture model cnns
F. Monti, D. Boscaini, J. Masci, E. Rodola, J. Svoboda, and M. M. Bronstein · 2017
Cited alongside, same era.
struc2vec: Learning node representations from structural identity
L. F. Ribeiro, P. H. Saverese, and D. R. Figueiredo · 2017
Cited alongside, same era.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2017
Cited alongside, same era.
Quaternion knowledge graph embedding
S. Zhang, Y. Tay, L. Yao, and Q. Liu · 2019
Later among the works it cites.
Meta-learning state-based eligibility traces for more sample-efficient policy evaluation
M. Zhao, S. Luan, I. Porada, X.-W. Chang, and D. Precup · 2019
Later among the works it cites.
Simple and deep graph convolutional networks
M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li · 2020
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Graph representation learning
W. L. Hamilton · 2020
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Reward propagation using graph convolutional networks
M. Klissarov and D. Precup · 2020
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Predict then propagate: Graph neural networks meet personalized pagerank
J. Klicpera, A. Bojchevski, and S. Günnemann · 2018
Cited alongside, same era.
Diffusion-based approximate value functions
M. Klissarov and D. Precup · 2018
Cited alongside, same era.
Deeper insights into graph convolutional networks for semi-supervised learning
Q. Li, Z. Han, and X. Wu · 2018
Cited alongside, same era.
Adaptive graph convolutional neural networks
R. Li, S. Wang, F. Zhu, and J. Huang · 2018
Cited alongside, same era.
Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto · 2018
Cited alongside, same era.
Representation learning on graphs with jumping knowledge networks
K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka · 2018
Cited alongside, same era.
Graph convolutional networks: Algorithms, applications and open challenges
S. Zhang, H. Tong, J. Xu, and R. Maciejewski · 2018
Cited alongside, same era.
Non-local graph neural networks
M. Liu, Z. Wang, and S. Ji · 2020
Later among the works it cites.
Training matters: Unlocking potentials of deeper graph convolutional neural networks
S. Luan, M. Zhao, X.-W. Chang, and D. Precup · 2020
Later among the works it cites.
S. Luan, M. Zhao, C. Hua, X.-W. Chang, and D. Precup · 2020
Later among the works it cites.
Geom-gcn: Geometric graph convolutional networks
H. Pei, B. Wei, K. C.-C. Chang, Y. Lei, and B. Yang · 2020
Later among the works it cites.
Graph neural networks with heterophily
J. Zhu, R. A. Rossi, A. Rao, T. Mai, N. Lipka, N. K. Ahmed, and D. Koutra · 2020
Later among the works it cites.
Beyond homophily in graph neural networks: Current limitations and effective designs
J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra · 2020
Later among the works it cites.
Generalizing graph neural networks beyond homophily
J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra · 2020
Later among the works it cites.
Beyond low-frequency information in graph convolutional networks
D. Bo, X. Wang, C. Shi, and H. Shen · 2021
Later among the works it cites.
Adaptive universal generalized pagerank graph neural network
E. Chien, J. Peng, P. Li, and O. Milenkovic · 2021
Later among the works it cites.
New benchmarks for learning on non-homophilous graphs
D. Lim, X. Li, F. Hohne, and S.-N. Lim · 2021
Later among the works it cites.
Is heterophily a real nightmare for graph neural networks to do node classification?
S. Luan, C. Hua, Q. Lu, J. Zhu, M. Zhao, S. Zhang, X.-W. Chang, and D. Precup · 2021
Later among the works it cites.
Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks
Y. Yan, M. Hashemi, K. Swersky, Y. Yang, and D. Koutra · 2021
Later among the works it cites.
A consciousness-inspired planning agent for model-based reinforcement learning
M. Zhao, Z. Liu, S. Luan, S. Zhang, D. Precup, and Y. Bengio · 2021
Later among the works it cites.
Graph neural networks intersect probabilistic graphical models: A survey
C. Hua, S. Luan, Q. Zhang, and J. Fu · 2022
Later among the works it cites.
When do we need gnn for node classification?
S. Luan, C. Hua, Q. Lu, J. Zhu, X.-W. Chang, and D. Precup · 2022
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Revisiting heterophily for graph neural networks
S. Luan, C. Hua, Q. Lu, J. Zhu, M. Zhao, S. Zhang, X.-W. Chang, and D. Precup · 2022
Later among the works it cites.
S. Luan, M. Zhao, C. Hua, X.-W. Chang, and D. Precup · 2022
Later among the works it cites.
S. Luan, C. Hua, M. Xu, Q. Lu, J. Zhu, X.-W. Chang, J. Fu, J. Leskovec, and D. Precup · 2023
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