Big Graph : Tools, Techniques, Issues, Challenges and Future Directions

Abstract

Analyzing interconnection structures among the data through the use of graph algorithms and graph analytics has been shown to provide tremendous value in many application domains (like social networks, protein networks, transportation networks, bibliographical networks, knowledge bases and many more). Nowadays, graphs with billions of nodes and trillions of edges have become very common. In principle, graph analytics is an important big data discovery technique. Therefore, with the increasing abundance of large scale graphs, designing scalable systems for processing and analyzing large scale graphs has become one of the timeliest problems facing the big data research community. In general, distributed processing of big graphs is a challenging task due to their size and the inherent irregular structure of graph computations. In this paper, we present a comprehensive overview of the state-of-the-art to better understand the challenges of developing very high-scalable graph processing systems. In addition, we identify a set of the current open research challenges and discuss some promising directions for future research.