Link prediction attempts to predict whether an unseen edge exists based on only a portion of
the graph. A flurry of methods has been created in recent years that attempt to make use of …

Link prediction, a fundamental task on graphs, has proven indispensable in various
applications, eg, friend recommendation, protein analysis, and drug interaction prediction …

Complex networks pervade various real-world systems, from the natural environment to
human societies. The essence of these networks is in their ability to transition and evolve …

Graph Foundation Model (GFM) is a new trending research topic in the graph domain,
aiming to develop a graph model capable of generalizing across different graphs and tasks …

Graph Neural Networks (GNNs) have shown great promise in learning node embeddings for
link prediction (LP). While numerous studies aim to improve the overall LP performance of …

Link prediction is a crucial task in graph machine learning, where the goal is to infer missing
or future links within a graph. Traditional approaches leverage heuristic methods based on …

Protein functions are characterized by interactions with proteins, drugs, and other
biomolecules. Understanding these interactions is essential for deciphering the molecular …

While Graph Neural Networks (GNNs) are remarkably successful in a variety of high-impact
applications, we demonstrate that, in link prediction, the common practices of including the …

We present a novel graph tokenization framework that generates structure-aware, semantic
node identifiers (IDs) in the form of a short sequence of discrete codes, serving as symbolic …

Link prediction is a critical task within the realm of graph machine learning. While recent
advancements mainly emphasize node representation learning, the rich information …