Graph neural networks (GNNs) are widely used in machine learning for graph-structured
data. Even though GNNs have achieved remarkable success in real-world applications …

Recently, many works studied the expressive power of graph neural networks (GNNs) by
linking it to the $1 $-dimensional Weisfeiler-Leman algorithm ($1\text {-}\mathsf {WL} $) …

Effective condition monitoring and fault diagnosis of rolling bearings, integral components of
rotating machinery, are crucial for ensuring equipment reliability. However, existing …

We consider the problem of traffic accident analysis on a road network based on road
network connections and traffic volume. Previous works have designed various deep …

Inspired by the success of foundation models in applications such as ChatGPT, as graph
data has been ubiquitous, one can envision the far-reaching impacts that can be brought by …

The Weisfeiler-Leman algorithm ($1 $-WL) is a well-studied heuristic for the graph
isomorphism problem. Recently, the algorithm has played a prominent role in understanding …

Multitask learning is widely used in practice to train a low-resource target task by
augmenting it with multiple related source tasks. Yet, naively combining all the source tasks …

PAC-Bayes is a well-established framework for analyzing generalization performance in
machine learning models. This framework provides a bound on the expected population …

Multitask learning is a widely used paradigm for training models on diverse tasks, with
applications ranging from graph neural networks to language model fine-tuning. Since tasks …

This work provides a theoretical framework for assessing the generalization error of graph
classification tasks via graph neural networks in the over-parameterized regime, where the …