Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link
Prediction (LP) tasks. This is due to limitations in expressive power such as the inability to …

We propose GNNLab, a sample-based GNN training system in a single machine multi-GPU
setup. GNNLab adopts a factored design for multiple GPUs, where each GPU is dedicated to …

Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-
based tasks. However, as mainstream GNNs are designed based on the neural message …

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

The self-supervised graph representation learning has achieved much success in recent
web based research and applications, such as recommendation system, social networks …

Graph Neural Networks (GNN) have evolved as powerful models for graph representation
learning. Many works have been proposed to support GNN training efficiently on GPU …

Cold-start problem is one of the long-standing challenges in recommender systems,
focusing on accurately modeling new or interaction-limited users or items to provide better …

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in
recommender systems. Nevertheless, the process of searching and ranking from a large …

Graph embeddings have become a key paradigm to learn node representations and
facilitate downstream graph analysis tasks. Many real-world scenarios such as online social …

The problem of k-nearest neighbor (k-NN) search is fundamental in graph databases, which
has numerous real-world applications, such as bioinformatics, computer vision, and software …