Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model
in broad application fields for their effectiveness in learning over graphs. To scale GNN …

Embodied AI agents that search for objects in large environments such as households often
need to make efficient decisions by predicting object locations based on partial information …

Link prediction, as a fundamental task for graph neural networks (GNNs), has boasted
significant progress in varied domains. Its success is typically influenced by the expressive …

Link prediction is a crucial problem in graph-structured data. Due to the recent success of
graph neural networks (GNNs), a variety of GNN-based models were proposed to tackle the …

Despite the remarkable achievements of Graph Neural Networks (GNNs) on graph
representation learning, few works have tried to use them to predict properties of subgraphs …

This paper explores the ability of Graph Neural Networks (GNNs) in learning various forms
of information for link prediction, alongside a brief review of existing link prediction methods …

Training deep graph neural networks (GNNs) poses a challenging task, as the performance
of GNNs may suffer from the number of hidden message-passing layers. The literature has …

Link prediction (LP) is a fundamental task in graph representation learning, with numerous
applications in diverse domains. However, the generalizability of LP models is often …

Link prediction is a fundamental problem in graph data. In its most realistic setting, the
problem consists of predicting missing or future links between random pairs of nodes from …

Recently, multiple models proposed for link prediction (LP) demonstrate impressive results
on benchmark datasets. However, many popular benchmark datasets often assume that …