9 citations · 21 across the 9 of their papers we have counts for
9 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…
A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process
Fuyang Li, Jiying Zhang, Xi Xiao +2
Kernels on discrete structures evaluate pairwise similarities between objects which capture semantics and inherent topology information. Existing kernels on discrete structures are…
GraphTTA: Test Time Adaptation on Graph Neural Networks
Guanzi Chen, Jiying Zhang, Xi Xiao +1
Recently, test time adaptation (TTA) has attracted increasing attention due to its power of handling the distribution shift issue in the real world. Unlike what has been developed…
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport
Jiying Zhang, Xi Xiao, Long-Kai Huang +2
Recently, the pretrain-finetuning paradigm has attracted tons of attention in graph learning community due to its power of alleviating the lack of labels problem in many real-world…
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…
Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs
Jiying Zhang, Fuyang Li, Xi Xiao +4
As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity from the graph learning community. However, commonly used frameworks in deep hypergraph le…
Learnable Hypergraph Laplacian for Hypergraph Learning
Jiying Zhang, Yuzhao Chen, Xi Xiao +2
HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph structured data. However, most existing conv…
Learnable Hypergraph Laplacian for Hypergraph Learning
Jiying Zhang, Yuzhao Chen, Xi Xiao +2
Hypergraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph-structured data. However, most existing conv…
Diversified Multiscale Graph Learning with Graph Self-Correction
Yuzhao Chen, Yatao Bian, Jiying Zhang +4
Though the multiscale graph learning techniques have enabled advanced feature extraction frameworks, the classic ensemble strategy may show inferior performance while encountering…