Recent breakthroughs in large language modeling have facilitated rigorous exploration of
their application in diverse tasks related to tabular data modeling, such as prediction, tabular …

Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …

Graphs are a powerful tool for representing and analyzing complex relationships in real-
world applications such as social networks, recommender systems, and computational …

The emergence of large-scale pre-trained language models has revolutionized various AI
research domains. Transformers-based Large Language Models (LLMs) have gradually …

Graph Neural Networks (GNNs) have shown promising potential in graph representation
learning. The majority of GNNs define a local message-passing mechanism, propagating …

Foundation models have emerged as critical components in a variety of artificial intelligence
applications, and showcase significant success in natural language processing and several …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications such as …

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …

Graph Transformer (GT) recently has emerged as a new paradigm of graph learning
algorithms, outperforming the previously popular Message Passing Neural Network (MPNN) …

Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph
domain, aiming to develop graph models trained on extensive and diverse data to enhance …