7 citations · 7 across the 5 of their papers we have counts for
5 papers
Graph Augmentation for Cross Graph Domain Generalization
Guanzi Chen, Jiying Zhang, Yang Li
Cross-graph node classification, utilizing the abundant labeled nodes from one graph to help classify unlabeled nodes in another graph, can be viewed as a domain generalization pro…
Attention-based Graph Convolution Fusing Latent Structures and Multiple Features for Graph Neural Networks
Yang Li, Yuichi Tanaka
We present an attention-based spatial graph convolution (AGC) for graph neural networks (GNNs). Existing AGCs focus on only using node-wise features and utilizing one type of atten…
Preventing Over-Smoothing for Hypergraph Neural Networks
Guanzi Chen, Jiying Zhang, Xi Xiao +1
In recent years, hypergraph learning has attracted great attention due to its capacity in representing complex and high-order relationships. However, current neural network approac…
Structure-Aware Multi-Hop Graph Convolution for Graph Neural Networks
Yang Li, Yuichi Tanaka
In this paper, we propose a spatial graph convolution (GC) to classify signals on a graph. Existing GC methods are limited to using the structural information in the feature space.…
Graph Routing between Capsules
Yang Li, Wei Zhao, Erik Cambria +2
Routing methods in capsule networks often learn a hierarchical relationship for capsules in successive layers, but the intra-relation between capsules in the same layer is less stu…