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07/03/2023
ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning
The recent contrastive learning methods, due to their effectiveness in r...
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05/17/2023
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
Large Language Models (LLMs), armed with billions of parameters, exhibit...
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03/13/2023
A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges
While deep learning has achieved great success on various tasks, the tas...
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12/20/2022
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning
Molecular representation learning is crucial for the problem of molecula...
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12/08/2022
TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
There has been an explosion of interest in designing various Knowledge G...
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10/19/2022
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations
The training of graph neural networks (GNNs) is extremely time consuming...
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10/14/2022
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
Large-scale graph training is a notoriously challenging problem for grap...
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08/04/2022
Differentially Private Counterfactuals via Functional Mechanism
Counterfactual, serving as one emerging type of model explanation, has a...
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10/28/2021
CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks
Despite the recent advances of graph neural networks (GNNs) in modeling ...
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09/23/2021
Orthogonal Graph Neural Networks
Graph neural networks (GNNs) have received tremendous attention due to t...
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08/30/2021
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Graph neural networks (GNNs), which learn the node representations by re...
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08/24/2021
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study
Training deep graph neural networks (GNNs) is notoriously hard. Besides ...
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07/06/2021
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
Graph neural networks (GNNs) integrate deep architectures and topologica...
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03/26/2021
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization
Human-designed data augmentation strategies have been replaced by automa...
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10/25/2020
Towards Interaction Detection Using Topological Analysis on Neural Networks
Detecting statistical interactions between input features is a crucial a...
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06/19/2020
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning
Outlier detection is an important data mining task with numerous practic...
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06/12/2020
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Graph neural networks (GNNs), which learn the representation of a node b...
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12/17/2019
Multi-Channel Graph Convolutional Networks
Graph neural networks (GNN) has been demonstrated to be effective in cla...
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09/07/2019
Auto-GNN: Neural Architecture Search of Graph Neural Networks
Graph neural networks (GNN) has been successfully applied to operate on ...
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06/19/2019