115 citations · 292 across the 27 of their papers we have counts for
6 papers · 1 filter
Graph Structure Learning with Variational Information Bottleneck
Qingyun Sun, Jianxin Li, Hao Peng +4
Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications. Most empirical studies of GNNs directly take the observed graph as input, assuming th…
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network
Xingcheng Fu, Jianxin Li, Jia Wu +6
Graph Neural Networks (GNNs) have been widely studied in various graph data mining tasks. Most existingGNNs embed graph data into Euclidean space and thus are less effective to cap…
Convex Sparse Blind Deconvolution
Qingyun Sun, David Donoho
In the blind deconvolution problem, we observe the convolution of an unknown filter and unknown signal and attempt to reconstruct the filter and signal. The problem seems impossibl…
A Robust and Generalized Framework for Adversarial Graph Embedding
Jianxin Li, Xingcheng Fu, Hao Peng +5
Graph embedding is essential for graph mining tasks. With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-qua…
A Recipe for Global Convergence Guarantee in Deep Neural Networks
Kenji Kawaguchi, Qingyun Sun
Existing global convergence guarantees of (stochastic) gradient descent do not apply to practical deep networks in the practical regime of deep learning beyond the neural tangent k…
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism
Qingyun Sun, Jianxin Li, Hao Peng +4
Graph representation learning has attracted increasing research attention. However, most existing studies fuse all structural features and node attributes to provide an overarching…