A transformation procedure of converting nodes, edges, and graph into a set of multidimensional vectors that optimally capture the characteristics graph structure.
Published in Chapter:
Graph Data Management, Modeling, and Mining
Karthik Srinivasan (University of Kansas, USA)
Copyright: © 2023
|Pages: 21
DOI: 10.4018/978-1-7998-9220-5.ch121
Abstract
A graph or a network is an abstract representation of a set of objects where some pairs are connected by links. Graph analytics is the systematic computational analysis of graphs/networks. In contrast to tabular data analysis, graph analytics requires a different set of tools, techniques, and algorithms tuned towards representation of the graph structure. With increasingly complex phenomena in today's world such as systems biology, epidemics, social networks, organizational collusions, international trade relationships, and internet of things, the importance of modeling such networked systems is more than ever. Therefore, graph analytics is a necessary toolkit in data science and machine learning warranting exclusive research enquiry and pedagogy. This article introduces the reader to the breadth of analytics tools, techniques, algorithms, and software. After reading this article, the reader should be able to identify problems that can use a network approach as well as develop corresponding graph-based analytics solutions.