Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

Graph analytical queries (GAQs) are becoming increasingly important in various domains,
including social networks, recommendation systems, and bioinformatics, among others …

Existing disaggregated memory (DM) systems face a problem of underutilized far memory
bandwidth, which greatly limits the data throughput when processing data-intensive …

Out-of-core random walk system has recently attracted a lot of attention as an economical
way to run billions of walkers over large graphs. However, existing out-of-core random walk …

GPU is a powerful accelerator for parallel computation. Graph sampling is a fundamental
technology for large-scale graph analysis and learning. To accelerate graph sampling using …

Subgraph counting is a fundamental component for many downstream applications such as
graph representation learning and query optimization. Since obtaining the exact count is …

Disaggregated architecture brings new opportunities to memory-consuming applications like
graph processing. It allows one to outspread memory access pressure from local to far …

Graph sampling prepares training samples for graph learning and can dominate the training
time. Due to the increasing algorithm diversity and complexity, existing sampling frameworks …

Disaggregated architecture brings new opportunities to memory-consuming applications like
graph processing. It allows one to outspread memory access pressure from local to far …

Random walk is a powerful tool for large-scale graph learning, but its high computational
demand presents a challenge. While GPUs can accelerate random walk tasks, current …