Hessian Distributed Ant Optimized Perron–Frobenius Eigen Centrality for Social Networks

Authors

P. V. Kumaraguru, Guru Nanak College, Chennai
Vidyavathi Kamalakkannan, Selvam College of Technology
H. L. Gururaj, Manipal Institute of Technology
Francesco Flammini, IDSIA Dalle Molle Institute for Artificial Intelligence

Document Type

Article

Publication Title

ISPRS International Journal of Geo-Information

Abstract

Terabytes of data are now being handled by an increasing number of apps, and rapid user decision-making is hampered by data analysis. At the same time, there is a rise in interest in big data analysis for social networks at the moment. Thus, adopting distributed multi-agent-based technology in an optimum way is one of the solutions to effective big data analysis for social networks. Studying the development of a social network helps users gain an understanding of interactions and relationships and guides them in making decisions. In this study, a method called Hessian Distributed Ant Optimized and Perron–Frobenius Eigen Centrality (HDAO-PFEC) is developed to analyze large amounts of data (i.e., Big Data) in a computationally accurate and efficient manner. Designing an adaptable Multi-Agent System architecture for large data analysis is the primary goal of HDAO-PFEC. Initially, using a Hessian Mutual Distributed Ant Optimization MapReduce model, comparable user interest tweets are produced in a computationally efficient manner. Eigen Vector Centrality is a measure of a node’s importance in a network (i.e., a social network), which allows association with other significant nodes (i.e., users), allowing for a greater effect on social networks. With this goal in mind, a MapReduce methodology in the Hadoop platform using Big Data, which enables quick and ordered calculations, is used in a distributed computing method to estimate the Eigen Vector Centrality value for each social network member. Lastly, extensive investigative experimental learning demonstrates the HDAO-PFEC method’s use and accuracy as well as its time and overhead on the well-known sentiment 140 dataset.

DOI

10.3390/ijgi12080316

Publication Date

8-1-2023

Recommended Citation

Kumaraguru, P. V.; Kamalakkannan, Vidyavathi; Gururaj, H. L.; and Flammini, Francesco, "Hessian Distributed Ant Optimized Perron–Frobenius Eigen Centrality for Social Networks" (2023). Open Access archive. 5392.
https://impressions.manipal.edu/open-access-archive/5392