Demystifying graph AI
Pethuru Raj, Nachamai Muthuraman · Institution of Engineering and Technology eBooks · 2022
Graphs are emerging as futuristic and flexible data structures that can fluently and fluidly model different relationships and processes over physical, biological, social, and information systems. Graph nodes or vertices represent the system's entities. Nodes are connected by edges/links, which represent relationships between those entities. Such an influencing representation helps to express and expose complex interdependencies in data.The Artificial Intelligence (AI) domain fundamentally represents a collection of pioneering algorithms and approaches to uncover and emit out human-like intelligence from data volumes through an iterative and insightful process of building, evaluating, and optimizing AI models. As indicated above, another interesting facet gaining prominence in the recent past is the aspect of data representation through enigmatic graph structures. This transition has resulted in solving a myriad of complex business problems. Now by applying proven and potential AI procedures and processes on graph data, the task of knowledge discovery out of data mountains is becoming simpler and speedier. The prediction accuracy and performance of AI models when applied on graph data show a lot of perceptible improvements. This strategic and subtle convergence is being widely touted as the Graph AI paradigm. In this chapter, we are to discuss what, how, and why Graph AI is acquiring all the attention and how this new paradigm is bound to be a trend-setter for the ensuing era of knowledge.