Fetching the paper…
Reading the bibliography…
Graph neural networks, a powerful deep learning tool to model graph-structured data, have demonstrated remarkable performance on numerous graph learning tasks.
Neural networks and their applications
C. M. Bishop · 1994
Earlier work this paper cites.
Unsupervised word sense disambiguation rivaling supervised methods
D. Yarowsky · 1995
Earlier work this paper cites.
Combining labeled and unlabeled data with co-training
A. Blum and T. Mitchell · 1998
Earlier work this paper cites.
The pagerank citation ranking: Bringing order to the web
L. Page, S. Brin, R. Motwani, and T. Winograd · 1999
Earlier work this paper cites.
The information bottleneck method
N. Tishby, F. C. Pereira, and W. Bialek · 2000
Earlier work this paper cites.
Diffusion kernels on graphs and other discrete structures
R. I. Kondor and J. Lafferty · 2002
Earlier work this paper cites.
Scaling personalized web search
G. Jeh and J. Widom · 2003
Earlier work this paper cites.
Local graph partitioning using pagerank vectors
R. Andersen, F. Chung, and K. Lang · 2006
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
Earlier work this paper cites.
Fraudar: Bounding graph fraud in the face of camouflage
B. Hooi, H. A. Song, A. Beutel, N. Shah, K. Shin, and C. Faloutsos · 2016
Earlier work this paper cites.
Variational graph auto-encoders
T. N. Kipf and M. Welling · 2016
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
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
Earlier work this paper cites.
Learning graph-level representation for drug discovery
J. Li, D. Cai, and X. He · 2017
Earlier work this paper cites.
Graph classification via deep learning with virtual nodes
T. Pham, T. Tran, H. Dam, and S. Venkatesh · 2017
Earlier work this paper cites.
Fastgcn: Fast learning with graph convolutional networks via importance sampling
J. Chen, T. Ma, and C. Xiao · 2018
Earlier work this paper cites.
Predict then propagate: Graph neural networks meet personalized pagerank
J. Klicpera, A. Bojchevski, and S. Günnemann · 2018
Earlier work this paper cites.
Deeper insights into graph convolutional networks for semi-supervised learning
Q. Li, Z. Han, and X.-M. Wu · 2018
Earlier work this paper cites.
Adversarial attack and defense on graph data: A survey
L. Sun, Y. Dou, C. Yang, J. Wang, P. S. Yu, L. He, and B. Li · 2018
Earlier work this paper cites.
A quest for structure: jointly learning the graph structure and semi-supervised classification
X. Wu, L. Zhao, and L. Akoglu · 2018
Earlier work this paper cites.
How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2018
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2018
Earlier work this paper cites.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
W.-L. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C.-J. Hsieh · 2019
Earlier work this paper cites.
Batch virtual adversarial training for graph convolutional networks
Z. Deng, Y. Dong, and J. Zhu · 2019
Earlier work this paper cites.
Deep anomaly detection on attributed networks
K. Ding, J. Li, R. Bhanushali, and H. Liu · 2019
Earlier work this paper cites.
Graph adversarial training: Dynamically regularizing based on graph structure
F. Feng, X. He, J. Tang, and T.-S. Chua · 2019
Earlier work this paper cites.
Learning discrete structures for graph neural networks
L. Franceschi, M. Niepert, M. Pontil, and X. He · 2019
Earlier work this paper cites.
Pre-training graph neural networks for generic structural feature extraction
Z. Hu, C. Fan, T. Chen, K.-W. Chang, and Y. Sun · 2019
Earlier work this paper cites.
K. Ishiguro, S.-i. Maeda, and M. Koyama · 2019
Earlier work this paper cites.
Diffusion improves graph learning
J. Klicpera, S. Weißenberger, and S. Günnemann · 2019
Earlier work this paper cites.
Weisfeiler and leman go neural: Higher-order graph neural networks
C. Morris, M. Ritzert, M. Fey, W. L. Hamilton, J. E. Lenssen, G. Rattan, and M. Grohe · 2019
Earlier work this paper cites.
Dropedge: Towards deep graph convolutional networks on node classification
Y. Rong, W. Huang, T. Xu, and J. Huang · 2019
Earlier work this paper cites.
A survey on image data augmentation for deep learning
C. Shorten and T. M. Khoshgoftaar · 2019
Earlier work this paper cites.
Deep graph infomax
P. Velickovic, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm · 2019
Earlier work this paper cites.
Manifold mixup: Better representations by interpolating hidden states
V. Verma, A. Lamb, C. Beckham, A. Najafi, I. Mitliagkas, D. Lopez-Paz, and Y. Bengio · 2019
Earlier work this paper cites.
Graphdefense: Towards robust graph convolutional networks
X. Wang, X. Liu, and C.-J. Hsieh · 2019
Earlier work this paper cites.
Simplifying graph convolutional networks
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger · 2019
Earlier work this paper cites.
Adversarial examples for graph data: deep insights into attack and defense
H. Wu, C. Wang, Y. Tyshetskiy, A. Docherty, K. Lu, and L. Zhu · 2019
Earlier work this paper cites.
Topology optimization based graph convolutional network
L. Yang, Z. Kang, X. Cao, D. Jin, B. Yang, and Y. Guo · 2019
Earlier work this paper cites.
Graphsaint: Graph sampling based inductive learning method
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna · 2019
Earlier work this paper cites.
Graph convolutional networks: a comprehensive review
S. Zhang, H. Tong, J. Xu, and R. Maciejewski · 2019
Earlier work this paper cites.
Bayesian graph convolutional neural networks for semi-supervised classification
Y. Zhang, S. Pal, M. Coates, and D. Ustebay · 2019
Earlier work this paper cites.
Layer-dependent importance sampling for training deep and large graph convolutional networks
D. Zou, Z. Hu, Y. Wang, S. Jiang, Y. Sun, and Q. Gu · 2019
Earlier work this paper cites.
Scaling graph neural networks with approximate pagerank
A. Bojchevski, J. Klicpera, B. Perozzi, A. Kapoor, M. Blais, B. Rózemberczki, M. Lukasik, and S. Günnemann · 2020
Earlier work this paper cites.
Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
D. Chen, Y. Lin, W. Li, P. Li, J. Zhou, and X. Sun · 2020
Earlier work this paper cites.
On graph neural networks versus graph-augmented mlps
L. Chen, Z. Chen, and J. Bruna · 2020
Earlier work this paper cites.
Trading personalization for accuracy: Data debugging in collaborative filtering
L. Chen, Y. Yao, F. Xu, M. Xu, and H. Tong · 2020
Earlier work this paper cites.
Scalable graph neural networks via bidirectional propagation
M. Chen, Z. Wei, B. Ding, Y. Li, Y. Yuan, X. Du, and J.-R. Wen · 2020
Earlier work this paper cites.
Learning on attribute-missing graphs
X. Chen, S. Chen, J. Yao, H. Zheng, Y. Zhang, and I. W. Tsang · 2020
Earlier work this paper cites.
Iterative deep graph learning for graph neural networks: Better and robust node embeddings
Y. Chen, L. Wu, and M. Zaki · 2020
Cited alongside, same era.
Be more with less: Hypergraph attention networks for inductive text classification
K. Ding, J. Wang, J. Li, D. Li, and H. Liu · 2020
Cited alongside, same era.
Graph prototypical networks for few-shot learning on attributed networks
K. Ding, J. Wang, J. Li, K. Shu, C. Liu, and H. Liu · 2020
Cited alongside, same era.
All you need is low (rank) defending against adversarial attacks on graphs
N. Entezari, S. A. Al-Sayouri, A. Darvishzadeh, and E. E. Papalexakis · 2020
Cited alongside, same era.
Graph random neural networks for semi-supervised learning on graphs
W. Feng, J. Zhang, Y. Dong, Y. Han, H. Luan, Q. Xu, Q. Yang, E. Kharlamov, and J. Tang · 2020
Cited alongside, same era.
Contrastive multi-view representation learning on graphs
Graphmix: Improved training of gnns for semi-supervised learning
V. Verma, M. Qu, K. Kawaguchi, A. Lamb, Y. Bengio, J. Kannala, and J. Tang · 2021
Later among the works it cites.
Graph structure estimation neural networks
R. Wang, S. Mou, X. Wang, W. Xiao, Q. Ju, C. Shi, and X. Xie · 2021
Later among the works it cites.
A dual-branch graph convolutional network on imbalanced node classification
X. Wang and J. Chen · 2021
Later among the works it cites.
Distance-wise prototypical graph neural network in node imbalance classification
Y. Wang, C. Aggarwal, and T. Derr · 2021
Later among the works it cites.
Mixup for node and graph classification
Y. Wang, W. Wang, Y. Liang, Y. Cai, and B. Hooi · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. Hassani and A. H. Khasahmadi · 2020
Cited alongside, same era.
Gpt-gnn: Generative pre-training of graph neural networks
Z. Hu, Y. Dong, K. Wang, K.-W. Chang, and Y. Sun · 2020
Cited alongside, same era.
Collaborative graph convolutional networks: Unsupervised learning meets semi-supervised learning
B. Hui, P. Zhu, and Q. Hu · 2020
Cited alongside, same era.
Sub-graph contrast for scalable self-supervised graph representation learning
Y. Jiao, Y. Xiong, J. Zhang, Y. Zhang, T. Zhang, and Y. Zhu · 2020
Cited alongside, same era.
Graph structure learning for robust graph neural networks
W. Jin, Y. Ma, X. Liu, X. Tang, S. Wang, and J. Tang · 2020
Cited alongside, same era.
Graph coarsening with preserved spectral properties
Y. Jin, A. Loukas, and J. JaJa · 2020
Cited alongside, same era.
Flag: Adversarial data augmentation for graph neural networks
K. Kong, G. Li, M. Ding, Z. Wu, C. Zhu, B. Ghanem, G. Taylor, and T. Goldstein · 2020
Cited alongside, same era.
L. Wu, H. Lin, Z. Gao, C. Tan, S. Li, et al · 2021
Later among the works it cites.
Self-supervised learning on graphs: Contrastive, generative, or predictive
L. Wu, H. Lin, C. Tan, Z. Gao, and S. Z. Li · 2021
Later among the works it cites.
Graph sanitation with application to node classification
Z. Xu, B. Du, and H. Tong · 2021
Later among the works it cites.
Graph adversarial self-supervised learning
L. Yang, L. Zhang, and W. Yang · 2021
Later among the works it cites.
Sparse graph attention networks
Y. Ye and S. Ji · 2021
Later among the works it cites.
Identity-aware graph neural networks
J. You, J. M. Gomes-Selman, R. Ying, and J. Leskovec · 2021
Later among the works it cites.
Graph contrastive learning automated
Y. You, T. Chen, Y. Shen, and Z. Wang · 2021
Later among the works it cites.
Decoupling the depth and scope of graph neural networks
H. Zeng, M. Zhang, Y. Xia, A. Srivastava, A. Malevich, R. Kannan, V. Prasanna, L. Jin, and R. Chen · 2021
Later among the works it cites.
Contrastive self-supervised learning for graph classification
J. Zeng and P. Xie · 2021
Later among the works it cites.
Improving the training of graph neural networks with consistency regularization
C. Zhang, Y. He, Y. Cen, Z. Hou, and J. Tang · 2021
Later among the works it cites.
Nested graph neural networks
M. Zhang and P. Li · 2021
Later among the works it cites.
Adaptive diffusion in graph neural networks
J. Zhao, Y. Dong, M. Ding, E. Kharlamov, and J. Tang · 2021
Later among the works it cites.
Heterogeneous graph structure learning for graph neural networks
J. Zhao, X. Wang, C. Shi, B. Hu, G. Song, and Y. Ye · 2021
Later among the works it cites.
Data augmentation for graph neural networks
T. Zhao, Y. Liu, L. Neves, O. Woodford, M. Jiang, and N. Shah · 2021
Later among the works it cites.
Graphsmote: Imbalanced node classification on graphs with graph neural networks
T. Zhao, X. Zhang, and S. Wang · 2021
Later among the works it cites.
Graph contrastive learning with adaptive augmentation
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang · 2021
Later among the works it cites.
Regularizing graph neural networks via consistency-diversity graph augmentations
D. Bo, B. Hu, X. Wang, Z. Zhang, C. Shi, and J. Zhou · 2022
Closest in time.
Improving graph neural network expressivity via subgraph isomorphism counting
G. Bouritsas, F. Frasca, S. P. Zafeiriou, and M. Bronstein · 2022
Closest in time.
H. Chen, S. Zhang, and G. Xu · 2022
Closest in time.
Meta propagation networks for graph few-shot semi-supervised learning
K. Ding, J. Wang, J. Caverlee, and H. Liu · 2022
Closest in time.
Adversarial graph contrastive learning with information regularization
S. Feng, B. Jing, Y. Zhu, and H. Tong · 2022
Closest in time.
Grand+: Scalable graph random neural networks
W. Feng, Y. Dong, T. Huang, Z. Yin, X. Cheng, E. Kharlamov, and J. Tang · 2022
Closest in time.
G-mixup: Graph data augmentation for graph classification
X. Han, Z. Jiang, N. Liu, and X. Hu · 2022
Closest in time.
Learning graph augmentations to learn graph representations
K. Hassani and A. H. Khasahmadi · 2022
Closest in time.
Analyzing heterogeneous networks with missing attributes by unsupervised contrastive learning
D. He, C. Liang, C. Huo, Z. Feng, D. Jin, L. Yang, and W. Zhang · 2022
Closest in time.
Graphmae: Self-supervised masked graph autoencoders
Z. Hou, X. Liu, Y. Dong, C. Wang, J. Tang, et al · 2022
Closest in time.
Condensing graphs via one-step gradient matching
W. Jin, X. Tang, H. Jiang, Z. Li, D. Zhang, J. Tang, and B. Yin · 2022
Closest in time.
Graph condensation for graph neural networks
W. Jin, L. Zhao, S. Zhang, Y. Liu, J. Tang, and N. Shah · 2022
Closest in time.
Differentiable graph module (dgm) for graph convolutional networks
A. Kazi, L. Cosmo, S.-A. Ahmadi, N. Navab, and M. Bronstein · 2022
Closest in time.
Augmentation-free self-supervised learning on graphs
N. Lee, J. Lee, and C. Park · 2022
Closest in time.
Graph neural network with curriculum learning for imbalanced node classification
X. Li, L. Wen, Y. Deng, F. Feng, X. Hu, L. Wang, and Z. Fan · 2022
Closest in time.
Local augmentation for graph neural networks
S. Liu, H. Dong, L. Li, T. Xu, Y. Rong, P. Zhao, J. Huang, and D. Wu · 2022
Closest in time.
Graph self-supervised learning: A survey
Y. Liu, M. Jin, S. Pan, C. Zhou, Y. Zheng, F. Xia, and P. Yu · 2022
Closest in time.
Gatsmote: Improving imbalanced node classification on graphs via attention and homophily
Y. Liu, Z. Zhang, Y. Liu, and Y. Zhu · 2022
Closest in time.
Cf-gnnexplainer: Counterfactual explanations for graph neural networks
A. Lucic, M. A. Ter Hoeve, G. Tolomei, M. De Rijke, and F. Silvestri · 2022
Closest in time.
Automated data augmentations for graph classification
Y. Luo, M. McThrow, W. Y. Au, T. Komikado, K. Uchino, K. Maruhash, and S. Ji · 2022
Closest in time.
Simple unsupervised graph representation learning
Y. Mo, L. Peng, J. Xu, X. Shi, and X. Zhu · 2022
Closest in time.
Graph transplant: Node saliency-guided graph mixup with local structure preservation
J. Park, H. Shim, and E. Yang · 2022
Closest in time.
Mgae: Masked autoencoders for self-supervised learning on graphs
Q. Tan, N. Liu, X. Huang, R. Chen, S.-H. Choi, and X. Hu · 2022
Closest in time.
Understanding over-squashing and bottlenecks on graphs via curvature
J. Topping, F. Di Giovanni, B. P. Chamberlain, X. Dong, and M. M. Bronstein · 2022
Closest in time.
Simgrace: A simple framework for graph contrastive learning without data augmentation
J. Xia, L. Wu, J. Chen, B. Hu, and S. Z. Li · 2022
Closest in time.
Self-supervised learning of graph neural networks: A unified review
Y. Xie, Z. Xu, J. Zhang, Z. Wang, and S. Ji · 2022
Closest in time.
Label-invariant augmentation for semi-supervised graph classification
H. Yue, C. Zhang, C. Zhang, and H. Liu · 2022
Closest in time.
Learning from counterfactual links for link prediction
T. Zhao, G. Liu, D. Wang, W. Yu, and M. Jiang · 2022
Closest in time.
Eliciting structural and semantic global knowledge in unsupervised graph contrastive learning
K. Ding, Y. Wang, Y. Yang, and H. Liu · 2023
Closest in time.