Fetching the paper…
Reading the bibliography…
Graphs are widely used to describe real-world objects and their interactions.
The Complexity of Finding Fixed-Radius Near Neighbors
J. L. Bentley, D. F. Stanat, and E. H. Williams Jr · 1977
Earlier work this paper cites.
Computational Geometry: An Introduction
F. P. Preparata and M. I. Shamos · 1985
Earlier work this paper cites.
Algebraic Graph Theory
N. Biggs, N. L. Biggs, and B. Norman · 1993
Earlier work this paper cites.
Spectral Graph Theory
F. R. Chung · 1997
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.
A Rank Minimization Heuristic with Application to Minimum Order System Approximation
M. Fuel, H. Hindi, and S. P. Boyd · 2001
Earlier work this paper cites.
Molecular Modelling: Principles and Applications
A. R. Leach · 2001
Earlier work this paper cites.
Diffusion Kernels on Graphs and Other Discrete Input Spaces
R. Kondor and J. D. Lafferty · 2002
Earlier work this paper cites.
Coordinate Descent Algorithms for Lasso Penalized Regression
T. T. Wu and K. Lange · 2008
Earlier work this paper cites.
Gradient-Based Algorithms with Applications to Signal Recovery Problems
A. Beck and M. Teboulle · 2010
Earlier work this paper cites.
A Singular Value Thresholding Algorithm for Matrix Completion
J.-F. Cai, E. J. Candès, and Z. Shen · 2010
Earlier work this paper cites.
Auto-Encoding Variational Bayes
D. P. Kingma and M. Welling · 2014
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
How to Learn a Graph from Smooth Signals
V. Kalofolias · 2016
Earlier work this paper cites.
Variational Inference: A Review for Statisticians
D. M. Blei, A. Kucukelbir, and J. D. McAuliffe · 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.
Categorical Reparameterization with Gumbel-Softmax
E. Jang, S. Gu, and B. Poole · 2017
Earlier work this paper cites.
Attention is All You Need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, U. Kaiser, and I. Polosukhin · 2017
Earlier work this paper cites.
Adversarial Attack on Graph Structured Data
H. Dai, H. Li, T. Tian, X. Huang, L. Wang, J. Zhu, and L. Song · 2018
Earlier work this paper cites.
Adaptive Graph Convolutional Neural Networks
R. Li, S. Wang, F. Zhu, and J. Huang · 2018
Earlier work this paper cites.
Learning Sparse Neural Networks through L0 Regularization
C. Louizos, M. Welling, and D. P. Kingma · 2018
Earlier work this paper cites.
Networks (Second Edition)
M. Newman · 2018
Earlier work this paper cites.
Graph Signal Processing: Overview, Challenges, and Applications
A. Ortega, P. Frossard, J. Kovacevic, J. M. F. Moura, and P. Vandergheynst · 2018
Earlier work this paper cites.
Learning Human-Object Interactions by Graph Parsing Neural Networks
S. Qi, W. Wang, B. Jia, J. Shen, and S.-C. Zhu · 2018
Earlier work this paper cites.
Graph Attention Networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
Earlier work this paper cites.
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
J. Zhang, X. Shi, J. Xie, H. Ma, I. King, and D.-Y. Yeung · 2018
Earlier work this paper cites.
Learning Graphs From Data: A Signal Representation Perspective
X. Dong, D. Thanou, M. G. Rabbat, and P. Frossard · 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.
Graph Representation Learning via Hard and Channel-Wise Attention Networks
H. Gao and S. Ji · 2019
Earlier work this paper cites.
Generating Classification Weights With GNN Denoising Autoencoders for Few-Shot Learning
S. Gidaris and N. Komodakis · 2019
Earlier work this paper cites.
Semi-supervised Learning with Graph Learning-Convolutional Networks
B. Jiang, Z. Zhang, D. Lin, and J. Tang · 2019
Earlier work this paper cites.
Diffusion Improves Graph Learning
J. Klicpera, S. Weißenberger, and S. Günnemann · 2019
Cited alongside, same era.
Attention Models in Graphs: A Survey
J. B. Lee, R. A. Rossi, S. Kim, N. K. Ahmed, and E. Koh · 2019
Cited alongside, same era.
Graph Learning Network: A Structure Learning Algorithm
D. S. Pilco and A. R. Rivera · 2019
Cited alongside, same era.
Sequential Recommender Systems: Challenges, Progress and Prospects
S. Wang, L. Hu, Y. Wang, L. Cao, Q. Z. Sheng, and M. A. Orgun · 2019
Cited alongside, same era.
Dynamic Graph CNN for Learning on Point Clouds
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon · 2019
Cited alongside, same era.
GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks
R. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec · 2019
Cited alongside, same era.
Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis
R. Li, H. Chen, F. Feng, Z. Ma, X. Wang, and E. Hovy · 2021
Closest in time.
BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis
X. Li, Y. Zhou, N. C. Dvornek, M. Zhang, S. Gao, J. Zhuang, D. Scheinost, L. H. Staib, P. Ventola, and J. S. Duncan · 2021
Closest in time.
Class-Attentive Diffusion Network for Semi-Supervised Classification
J. Lim, D. Um, H. J. Chang, D. U. Jo, and J. Y. Choi · 2021
Closest in time.
Learning to Drop: Robust Graph Neural Network via Topological Denoising
D. Luo, W. Cheng, W. Yu, B. Zong, J. Ni, H. Chen, and X. Zhang · 2021
Closest in time.
Predicting Molecular Conformation via Dynamic Graph Score Matching
S. Luo, C. Shi, M. Xu, and J. Tang · 2021
Closest in time.
GraphiT: Encoding Graph Structure in Transformers
G. Mialon, D. Chen, M. Selosse, and J. Mairal · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification
Y. Zhang, S. Pal, M. Coates, and D. Üstebay · 2019
Cited alongside, same era.
Robust Graph Convolutional Networks Against Adversarial Attacks
D. Zhu, Z. Zhang, P. Cui, and W. Zhu · 2019
Cited alongside, same era.
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
Y. Chen, L. Wu, and M. J. Zaki · 2020
Cited alongside, same era.
Latent Patient Network Learning for Automatic Diagnosis
L. Cosmo, A. Kazi, S.-A. Ahmadi, N. Navab, and M. Bronstein · 2020
Cited alongside, same era.
A Generalization of Transformer Networks to Graphs
V. P. Dwivedi and X. Bresson · 2020
Cited alongside, same era.
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
P. Elinas, E. V. Bonilla, and L. C. Tiao · 2020
Cited alongside, same era.
Closest in time.
E(n) equivariant graph neural networks
V. G. Satorras, E. Hoogeboom, and M. Welling · 2021
Closest in time.
Energy-based Learning for Scene Graph Generation
M. Suhail, A. Mittal, B. Siddiquie, C. Broaddus, J. Eledath, G. Medioni, and L. Sigal · 2021
Closest in time.
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
S. Suresh, P. Li, C. Hao, and J. Neville · 2021
Closest in time.
Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks
Y. Tian, G. Chen, Y. Song, and X. Wan · 2021
Closest in time.
Graph Sparsification via Meta-Learning
G. Wan and H. Kokel · 2021
Closest in time.
Multi-hop Attention Graph Neural Networks
G. Wang, R. Ying, J. Huang, and J. Leskovec · 2021
Closest in time.
Speedup Robust Graph Structure Learning with Low-Rank Information
H. Xu, L. Xiang, J. Yu, A. Cao, and X. Wang · 2021
Closest in time.
Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark
C. Yang, Y. Xiao, Y. Zhang, Y. Sun, and J. Han · 2021
Closest in time.
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
M. Yasunaga, H. Ren, A. Bosselut, P. Liang, and J. Leskovec · 2021
Closest in time.
Do Transformers Really Perform Bad for Graph Representation?
C. Ying, T. Cai, S. Luo, S. Zheng, G. Ke, D. He, Y. Shen, and T.-Y. Liu · 2021
Closest in time.
Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval
X. Yu, W. Xu, Z. Cui, S. Wu, and L. Wang · 2021
Closest in time.
Adaptive Diffusion in Graph Neural Networks
J. Zhao, Y. Dong, M. Ding, E. Kharlamov, and J. Tang · 2021
Closest in time.
Heterogeneous Graph Structure Learning for Graph Neural Networks
J. Zhao, X. Wang, C. Shi, B. Hu, G. Song, and Y. Ye · 2021
Closest in time.
Data Augmentation for Graph Neural Networks
T. Zhao, Y. Liu, L. Neves, O. Woodford, M. Jiang, and N. Shah · 2021
Closest in time.
Graph Contrastive Learning with Adaptive Augmentation
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang · 2021
Closest in time.
How Attentive are Graph Attention Networks?
S. Brody, U. Alon, and E. Yahav · 2022
Closest in time.
Graph Neural Networks with Learnable Structural and Positional Representations
V. P. Dwivedi, A. T. Luu, T. Laurent, Y. Bengio, and X. Bresson · 2022
Closest in time.
Differentiable Scaffolding Tree for Molecule Optimization
T. Fu, W. Gao, C. Xiao, J. Yasonik, C. W. Coley, and J. Sun · 2022
Closest in time.
FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation
X. Kan, H. Cui, J. Lukemire, Y. Guo, and C. Yang · 2022
Closest in time.
Constrained Structure Learning for Scene Graph Generation
D. Liu, M. Bober, and J. Kittler · 2022
Closest in time.
Towards Unsupervised Deep Graph Structure Learning
Y. Liu, Y. Zheng, D. Zhang, H. Chen, H. Peng, and S. Pan · 2022
Closest in time.
Graph Structure Learning with Variational Information Bottleneck
Q. Sun, J. Li, H. Peng, J. Wu, X. Fu, C. Ji, and P. S. Yu · 2022
Closest in time.
Mining Fine-grained Semantics via Graph Neural Networks for Evidence-based Fake News Detection
W. Xu, J. Wu, Q. Liu, S. Wu, and L. Wang · 2022
Closest in time.
A Unified Structure Learning Framework for Graph Attention Networks
J. Yuan, M. Cao, H. Cheng, H. Yu, J. Xie, and C. Wang · 2022
Closest in time.