A graph neural network (GNN) enables deep learning on structured graph data. There are
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …

Making sense of large graph datasets is a fundamental and challenging process that
advances science, education and technology. We survey research on graph exploration and …

In this paper, we present GridGraph, a system for processing large-scale graphs on a single
machine. Grid-Graph breaks graphs into 1D-partitioned vertex chunks and 2D-partitioned …

Graph mining is an important category of graph algorithms that aim to discover structural
patterns such as cliques and motifs in a graph. While a great deal of work has been done …

We describe GraFBoost, a flash-based architecture with hardware acceleration for external
analytics of multi-terabyte graphs. We compare the performance of GraFBoost with 1 GB of …

Single-PC, disk-based processing of big graphs has recently gained much popularity. At the
core of an efficient disk-based system is a well-designed partition structure that can minimize …

There is more than a decade-long history of using static analysis to find bugs in systems
such as Linux. Most of the existing static analyses developed for these systems are simple …

Recent studies show that graph processing systems on a single machine can achieve
competitive performance compared with cluster-based graph processing systems. In this …

Out-of-core graph processing systems are well-optimized to maintain sequential locality on
disk and minimize the amount of disk I/O per iteration. Even though the sparsity in real-world …

Graphs are a tremendously suitable data representations that model the relationships of
entities in many application domains, such as recommendation systems, machine learning …