MACHINE LEARNING BASED ON GRAPHS FOR PREDICTIVE MODELING IN INTRICATE NETWORK DATA

Authors

Dr. Sandip D. Satav, Associate Professor,
Department of Information Technology, JSCOE, Pune, India.
Dr. Poonam D Lambhate, Professor,
Computer Engineering, JSCOE, Pune, India.
Dr. Chandraprabha A Manjare, Professor,
Electronics & Telecommunication Engineering, JSCOE, Pune, India.
Dr. Shailesh M Hambarde, Associate Professor,
Electronics & Telecommunication Engineering, JSCOE, Pune, India.
Dr. Aparna S Hambarde, Assistant Professor,
Computer Engineering, KJ’s COE, India.
Mrs. Aarti S Satav, Manager,
SBI, Pune, India.

Abstract

As contemporary network systems become more intricate and interconnected, machine learning has emerged as a potent instrument for predictive modeling. Because of their capacity to manage complex network data structures, including social networks, biological networks, communication networks, and Internet of Things systems, graph-based machine learning models have become increasingly popular among these. In order to increase prediction accuracy, graph-based techniques offer a framework for illustrating the connections between items in a network. This paper offers a thorough investigation of machine learning methods for predictive modeling in complex networks that make use of graph structures. The study assesses important graph-based algorithms, looks at how they are used in different fields, and talks about the difficulties and potential avenues for further research.