A study of graph analytics for massive datasets

Abstract

The analysis and research of data which can be altered into a comprehensive graph is referred to as "graph analytics." Graph-based data analytics is a budding field in both data mining and data visualization and is applied for a wide variety of applications such as network protection, banking, and healthcare, both multi-disciplinary and high impact applications [5]. Despite the fact that many methods have been developed in the past to analyze unstructured collections of multidimensional objects, graph analytic technologies are a recent trend that poses several challenges, not only in terms of the output of algorithms that are related to data mining that facilitate algorithmic computational data discovery [3]. Graph analytics primarily aimed to evaluate graph oriented structured data in order to uncover answers to queries (e.g. Identify the person who is the most prominent person in a community? What are the main technology nodes for better practice and decision-making on the internet and urban networks?)Analysis of graphs has always attracted and has always been an important topic for researchers in the history of computing; however, the rise of the uses of advanced analytics for large amounts of semi-structured or unstructured data and the revolution of big data has lately picked up the interest of the information systems community [1]. The qualitative effect of data, as well as the impact of graph analytics technology on organizations, has affected the requirements for business outcomes. Graph analytics for big data can not only recognize but also visualize crucial insights in big data. Furthermore, graph analytics may assist in identifying associations between different types of data and determining their existence and meaning within the context [2].In this chapter, we will present the fundamentals of graph analytics and how graphs are related to big data. The chapter will also show some of the most common graph databases and discuss various big data graph analytics approaches which use the massive datasets, as well as different frameworks for each approach. In the latter part of the chapter, various issues and challenges related to big graph analytics will be addressed. A case study for implementation of graph analytics using python will also be discussed.