729 citations · 807 across the 13 of their papers we have counts for
9 papers · 1 filter
Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix
Shaogao Lv, Gang Wen, Shiyu Liu +2
To improve the robustness of graph neural networks (GNN), graph structure learning (GSL) has attracted great interest due to the pervasiveness of noise in graph data. Many approach…
Stability and Generalization of lp-Regularized Stochastic Learning for GCN
Shiyu Liu, Linsen Wei, Shaogao Lv +1
Graph convolutional networks (GCN) are viewed as one of the most popular representations among the variants of graph neural networks over graph data and have shown powerful perform…
Permutation Equivariant Graph Framelets for Heterophilous Graph Learning
Jianfei Li, Ruigang Zheng, Han Feng +2
The nature of heterophilous graphs is significantly different from that of homophilous graphs, which causes difficulties in early graph neural network models and suggests aggregati…
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…
How Framelets Enhance Graph Neural Networks
Xuebin Zheng, Bingxin Zhou, Junbin Gao +4
This paper presents a new approach for assembling graph neural networks based on framelet transforms. The latter provides a multi-scale representation for graph-structured data. We…
Path Integral Based Convolution and Pooling for Graph Neural Networks
Zheng Ma, Junyu Xuan, Yu Guang Wang +2
Graph neural networks (GNNs) extends the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and poolin…
MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning
Xuebin Zheng, Bingxin Zhou, Ming Li +2
Graph Neural Networks (GNNs) have recently caught great attention and achieved significant progress in graph-level applications. In this paper, we propose a framework for graph neu…
Haar Graph Pooling
Yu Guang Wang, Ming Li, Zheng Ma +3
Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks. In these tasks, graph pooling is a critical ingredient by which GNNs…
PAN: Path Integral Based Convolution for Deep Graph Neural Networks
Zheng Ma, Ming Li, Yuguang Wang
Convolution operations designed for graph-structured data usually utilize the graph Laplacian, which can be seen as message passing between the adjacent neighbors through a generic…