Data-intensive applications dominated by random accesses to large working sets fail to
utilize the computing power of modern processors. Graph random walk, an indispensable …

Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in
machine learning tasks. The increasing availability of billion-edge graphs underscores the …

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 …

Graphs are a representation of structured data that captures the relationships between sets
of objects. With the ubiquity of available network data, there is increasing industrial and …

Graph embedding maps graph nodes to low-dimensional vectors and is widely used in
machine learning tasks. The increasing availability of billion-edge graphs underscores the …

Representation learning using network embedding has received tremendous attention due
to its efficacy to solve downstream tasks. Popular embedding methods (such as deepwalk …

As a fundamental tool for graph analysis, random walk receives extensive attention in both
industry and academia. For computing massive random walks, recent works show that GPUs …

Graphics processing units (GPUs) are commonly used to accelerate training and inference
of deep neural networks (DNNs). Modern cloud nodes are shared by multiple users to …

Graphs are ubiquitous today and are a fundamental data structure to represent objects and
their relations in various domains, eg, social science, citation analysis, weblink analysis, and …

Graph neural networks (GNNs) have been emerging as powerful learning tools for
recommendation systems, social networks and knowledge graphs. In these domains, the …