In the big data era, much real-world data can be naturally represented as graphs.
Consequently, many application domains can be modeled as graph processing. Graph …

Large-scale graph processing is gaining increasing attentions in many domains. Meanwhile,
FPGA provides a power-efficient and highly parallel platform for many applications, and has …

Due to the growing need to process large graph and network datasets created by modern
applications, recent years have witnessed a surging interest in developing big graph …

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

Mining from a big graph those subgraphs that satisfy certain conditions is useful in many
applications such as community detection and subgraph matching. These problems have a …

Many graph processing systems have been recently developed for many-core processors.
However, for iterative graph processing, due to the dependencies between vertices' states …

Many systems are recently proposed for large-scale iterative graph analytics on a single
machine with GPU accelerators. Despite of many research efforts, for iterative directed graph …

Graph processing is becoming commonplace in many applications to analyze huge
datasets. Much of the prior work in this area has assumed I/O devices with considerable …

Pioneered by Google's Pregel, many distributed systems have been developed for large-
scale graph analytics. These systems expose the user-friendly" think like a vertex" …

Despite its popularity, SimRank is computationally costly, in both time and space. In
particular, its recursive nature poses a great challenge in using modern distributed …