出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Analyzing interconnection structures among the data through the use of graph algorithms andgraph analytics has been shown to provide tremendous value in many application domains (likesocial networks, protein networks, transportation networks, bibliographical networks,knowledge bases and many more). Nowadays, graphs with billions of nodes and trillions ofedges have become very common. In principle, graph analytics is an important big datadiscovery technique. Therefore, with the increasing abundance of large scale graphs, designingscalable systems for processing and analyzing large scale graphs has become one of thetimeliest problems facing the big data research community. In general, distributed processing ofbig graphs is a challenging task due to their size and the inherent irregular structure of graphcomputations. In this paper, we present a comprehensive overview of the state-of-the-art tobetter understand the challenges of developing very high-scalable graph processing systems. Inaddition, we identify a set of the current open research challenges and discuss some promisingdirections for future research.
