Graph technology: a detailed study of trending techniques and technologies of graph analytics

Abstract

Graphs are information structures to depict connections and communications between elements in complex frameworks. Graph analytics has been in use for a long time in the field of data analytics. Its main purpose is to create a database of interconnected entities and to model the relationships and processes in various information systems. In the field of data science and analytics, it is the graph analytics, an alternative method of analysis that uses the process of abstraction and this abstraction is called a graph model which helps the analyst to analyze the whole data or results in a summarized form that reduces the analytics complexities. Many organizations use the Graph model to leverage analysis in marketing or social networks. Graph storage is also an important fact along with graph analytics. The underlying structure of any database in which graph data is stored is often called graph storage as native and non-native graph storage. This chapter is going to explain graph analytics as well as graph storage in detail with the knowledge of various graph logical approaches and looks at existing graph storage and computational advances. This research provides analytical insights about the various graph analytical technologies used globally and shows a comparison between existing graph storage and computer technology. This research additionally evaluates the performance, qualities, and impediments of different graph databases and graph processing models.