9 citations · 51 across the 24 of their papers we have counts for
7 papers · 1 filter
Graph Contrastive Learning with Implicit Augmentations
Huidong Liang, Xingjian Du, Bilei Zhu +3
Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e.g., randomly adding or dropping edges and nodes). Nevertheless, alterin…
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP
Jie Chen, Shouzhen Chen, Mingyuan Bai +3
The message-passing mechanism helps Graph Neural Networks (GNNs) achieve remarkable results on various node classification tasks. Nevertheless, the recursive nodes fetching and agg…
Generalized energy and gradient flow via graph framelets
Andi Han, Dai Shi, Zhiqi Shao +1
In this work, we provide a theoretical understanding of the framelet-based graph neural networks through the perspective of energy gradient flow. By viewing the framelet-based mode…
Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing
Jie Chen, Weiqi Liu, Zhizhong Huang +3
The performance of GNNs degrades as they become deeper due to the over-smoothing. Among all the attempts to prevent over-smoothing, residual connection is one of the promising meth…
Embedding Graphs on Grassmann Manifold
Bingxin Zhou, Xuebin Zheng, Yu Guang Wang +2
Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction. Given the non-Euclidean structural…
Differentially private Riemannian optimization
Andi Han, Bamdev Mishra, Pratik Jawanpuria +1
In this paper, we study the differentially private empirical risk minimization problem where the parameter is constrained to a Riemannian manifold. We introduce a framework of diff…