96 citations · 415 across the 46 of their papers we have counts for
8 papers · 1 filter
Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification
Yu Wang, Charu Aggarwal, Tyler Derr
Recent years have witnessed the significant success of applying graph neural networks (GNNs) in learning effective node representations for classification. However, current GNNs ar…
Syntax Matters! Syntax-Controlled in Text Style Transfer
Zhiqiang Hu, Roy Ka-Wei Lee, Charu C. Aggarwal
Existing text style transfer (TST) methods rely on style classifiers to disentangle the text's content and style attributes for text style transfer. While the style classifier play…
Jointly Attacking Graph Neural Network and its Explanations
Wenqi Fan, Wei Jin, Xiaorui Liu +7
Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Despite the great success, recent studies have shown that GNNs are highly vulnerable to adve…
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang +7
Deep learning's performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does…
NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs
Enyan Dai, Charu Aggarwal, Suhang Wang
Graph Neural Networks (GNNs) have achieved promising results for semi-supervised learning tasks on graphs such as node classification. Despite the great success of GNNs, many real-…
Graph Feature Gating Networks
Wei Jin, Xiaorui Liu, Yao Ma +3
Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs. Most GNNs follow a message-passing scheme where…
SetConv: A New Approach for Learning from Imbalanced Data
Yang Gao, Yi-Fan Li, Yu Lin +2
For many real-world classification problems, e.g., sentiment classification, most existing machine learning methods are biased towards the majority class when the Imbalance Ratio (…
Meta-Learning with Graph Neural Networks: Methods and Applications
Debmalya Mandal, Sourav Medya, Brian Uzzi +1
Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. H…