With the growth of the inter-connectivity of the world, Big Graph has become a popular
emerging technology. For instance, social media (Facebook, Twitter). Prominent examples …

Graph is an important data model, which can describe structural information including
dependent relationship, such as transportation network, social network and webpage …

We investigate runtime environment characteristics and explore the challenges of
conventional in-memory graph processing. This system-level analysis includes empirical …

With the ubiquity of graphs to represent large, sparse data sets, a recent focus has been
placed on making graph processing more efficient. Processing-in-Memory (PIM) is an …

Efficient computing for large-scale graph data is central to a broad spectrum of data-
intensive applications [1]. To achieve high performance with low communication and …

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

The explosion of digital data and the ever-growing need for fast data analysis have made in-
memory big-data processing in computer systems increasingly important. In particular, large …

Big data graph analytics is the future of high performance computing and key to many
current and future applications. There is a growing demand for high performance graph …

Large graph computing system is a key tool in big data computing. It can be applied to a
variety of big data applications, such as social networks, web page search, and protein …

With the advent of big-data, processing large graphs quickly has become increasingly
important. Most existing approaches either utilize in-memory processing techniques that can …