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

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 …

Many real-world graphs are temporal in nature, where the temporal information indicates
when a particular edge is changed (eg, edge insertion and deletion). Performing random …

Distributed graph analysis usually partitions a large graph into multiple small-sized
subgraphs and distributes them into a cluster of machines for computing. Therefore, graph …

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 …

Graph dynamic random walks (GDRWs) have recently emerged as a powerful paradigm for
graph analytics and learning applications, including graph embedding and graph neural …

Dynamic graph random walk (DGRW) emerges as a practical tool for capturing structural
relations within a graph. Effectively executing DGRW on GPU presents certain challenges …

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

Many real-world networks are characterized by being temporal and dynamic, wherein the
temporal information signifies the changes in connections, such as the addition or removal …

Graph sampling and random walk algorithms are playing increasingly important roles today
because they can significantly reduce graph size while preserving structural information …