Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Good: A graph out-of-distribution benchmark
Abstract Out-of-distribution (OOD) learning deals with scenarios in which training and test
data follow different distributions. Although general OOD problems have been intensively …
data follow different distributions. Although general OOD problems have been intensively …
Dynamic graph neural networks under spatio-temporal distribution shift
Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities
by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to …
by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to …
Unleashing the power of graph data augmentation on covariate distribution shift
The issue of distribution shifts is emerging as a critical concern in graph representation
learning. From the perspective of invariant learning and stable learning, a recently well …
learning. From the perspective of invariant learning and stable learning, a recently well …
Environment-aware dynamic graph learning for out-of-distribution generalization
Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-
temporal patterns on dynamic graphs. However, existing works fail to generalize under …
temporal patterns on dynamic graphs. However, existing works fail to generalize under …
Spectral invariant learning for dynamic graphs under distribution shifts
Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts
that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution …
that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution …
A Simple Data Augmentation for Graph Classification: A Perspective of Equivariance and Invariance
In graph classification, the out-of-distribution (OOD) issue is attracting great attention. To
address this issue, a prevailing idea is to learn stable features, on the assumption that they …
address this issue, a prevailing idea is to learn stable features, on the assumption that they …
Graph domain adaptation with dual-branch encoder and two-level alignment for whole slide image-based survival prediction
In recent years, histopathological whole slide image (WSI)-based survival analysis has
attracted much attention in medical image analysis. In practice, WSIs usually come from …
attracted much attention in medical image analysis. In practice, WSIs usually come from …
Out-of-distribution generalized dynamic graph neural network with disentangled intervention and invariance promotion
Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities
by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to …
by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to …
Wasserstein distance regularized graph neural networks
Distribution shift widely exists in graph representation learning and often reduces model
performance. This work investigates how to improve the performance of a graph neural …
performance. This work investigates how to improve the performance of a graph neural …
Mario: Model agnostic recipe for improving ood generalization of graph contrastive learning
In this work, we investigate the problem of out-of-distribution (OOD) generalization for
unsupervised learning methods on graph data. To improve the robustness against such …
unsupervised learning methods on graph data. To improve the robustness against such …