Lingxiao Zhao
Lingxiao Zhao
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Pairnorm: Tackling oversmoothing in gnns
L Zhao, L Akoglu
arXiv preprint arXiv:1909.12223, 2019
Beyond homophily in graph neural networks: Current limitations and effective designs
J Zhu, Y Yan, L Zhao, M Heimann, L Akoglu, D Koutra
Advances in Neural Information Processing Systems 33, 7793-7804, 2020
Graph unrolling networks: Interpretable neural networks for graph signal denoising
S Chen, YC Eldar, L Zhao
IEEE Transactions on Signal Processing 69, 3699-3713, 2021
A quest for structure: Jointly learning the graph structure and semi-supervised classification
X Wu, L Zhao, L Akoglu
Proceedings of the 27th ACM international conference on information and …, 2018
Generalizing graph neural networks beyond homophily
J Zhu, Y Yan, L Zhao, M Heimann, L Akoglu, D Koutra
arXiv preprint arXiv:2006.11468, 2020
From stars to subgraphs: Uplifting any GNN with local structure awareness
L Zhao, W Jin, L Akoglu, N Shah
arXiv preprint arXiv:2110.03753, 2021
Graph condensation for graph neural networks
W Jin, L Zhao, S Zhang, Y Liu, J Tang, N Shah
arXiv preprint arXiv:2110.07580, 2021
On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights
L Zhao, L Akoglu
Big Data, 2021
Sign and Basis Invariant Networks for Spectral Graph Representation Learning
D Lim, J Robinson, L Zhao, T Smidt, S Sra, H Maron, S Jegelka
arXiv preprint arXiv:2202.13013, 2022
Connecting Graph Convolutional Networks and Graph-Regularized PCA
L Zhao, L Akoglu
arXiv preprint arXiv:2006.12294, 2020
Fast Attributed Graph Embedding via Density of States
S Sawlani, L Zhao, L Akoglu
2021 IEEE International Conference on Data Mining (ICDM), 559-568, 2021
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution
X Ding, L Zhao, L Akoglu
arXiv preprint arXiv:2206.07647, 2022
Connecting Graph Convolution and Graph PCA
L Zhao, L Akoglu
Approximate inference with Graph Neural Networks
L Zhao, K Korovina, W Si, M Cheung
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