The world is becoming a more conjunct place and the number of data sources such as
social networks, online transactions, web search engines, and mobile devices is increasing …

Non-volatile main memory (NVRAM) technologies provide an attractive set of features for
large-scale graph analytics, including byte-addressability, low idle power, and improved …

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 …

The challenge of executing extensive graph analyses in-memory intensifies with growing
graph sizes. This has given rise to disk-based external graph analytics systems that prioritize …

Graph processing model is being adopted extensively in various domains such as online
gaming, social media, scientific computing and Internet of Things (IoT). Since general …

Data caching and sharing is an effective approach for achieving high performance to many
applications in shared platforms such as the cloud. DRAM and SSD are two popular caching …

Abstract We introduce the Read-Only Semi-External (ROSE) Model for the design and
analysis of algorithms on large graphs. As in the well-studied semi-external model for graph …

Recent studies showed that single-machine graph processing systems can be as highly
competitive as cluster-based approaches on large-scale problems. While several out-of-core …

Graph analytics are at the heart of a broad range of applications such as drug discovery,
page ranking, and recommendation systems. When graph size exceeds memory size, out-of …

Graph datasets exceed the in-memory capacity of most standalone machines. Traditionally,
graph frameworks have overcome memory limitations through scale-out, distributing …