In the realm of Graph Neural Network (GNNs), two intriguing research directions have
recently emerged: Subgraph GNNs and Graph Transformers. These approaches have …

Predicting the trajectory of the ego vehicle is a critical task for autonomous vehicles. Even
though traffic regulations have defined boundaries, various behaviors of the agents in real …

Recent advances in integrating positional and structural encodings (PSEs) into graph neural
networks (GNNs) have significantly enhanced their performance across various graph …

With the emergence of data marketplaces, the demand for methods to assess the value of
data has increased significantly. While numerous techniques have been proposed for this …

Transformers serve as the backbone architectures of Foundational Models, where a domain-
specific tokenizer helps them adapt to various domains. Graph Transformers (GTs) have …

Self-supervised Learning (SSL) has emerged as a powerful technique in pre-training deep
learning models without relying on expensive annotated labels, instead leveraging …

Graph neural networks have inherent representational limitations due to their message-
passing structure. Recent work has suggested that these limitations can be overcome by …

Unsupervised pre-training on vast amounts of graph data is critical in real-world applications
wherein labeled data is limited, such as molecule properties prediction or materials science …

Predicting the trajectory of an ego vehicle is a critical component of autonomous driving
systems. Current state-of-the-art methods typically rely on Deep Neural Networks (DNNs) …

Graph neural networks (GNNs) employing message passing for graph classification are
inherently limited by the expressive power of the Weisfeiler-Leman (WL) test for graph …