Does invariant graph learning via environment augmentation learn invariance?
Invariant graph representation learning aims to learn the invariance among data from
different environments for out-of-distribution generalization on graphs. As the graph …
different environments for out-of-distribution generalization on graphs. As the graph …
Survey on Trustworthy Graph Neural Networks: From A Causal Perspective
Graph Neural Networks (GNNs) have emerged as powerful representation learning tools for
capturing complex dependencies within diverse graph-structured data. Despite their …
capturing complex dependencies within diverse graph-structured data. Despite their …
Mprompt: Exploring multi-level prompt tuning for machine reading comprehension
The large language models have achieved superior performance on various natural
language tasks. One major drawback of such approaches is they are resource-intensive in …
language tasks. One major drawback of such approaches is they are resource-intensive in …
Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning
Graph contrastive learning (GCL), standing as the dominant paradigm in the realm of graph
pre-training, has yielded considerable progress. Nonetheless, its capacity for out-of …
pre-training, has yielded considerable progress. Nonetheless, its capacity for out-of …
IENE: Identifying and Extrapolating the Node Environment for Out-of-Distribution Generalization on Graphs
H Yang, X Pei, K Yuan - arXiv preprint arXiv:2406.00764, 2024 - arxiv.org
Due to the performance degradation of graph neural networks (GNNs) under distribution
shifts, the work on out-of-distribution (OOD) generalization on graphs has received …
shifts, the work on out-of-distribution (OOD) generalization on graphs has received …