Hessian Distributed Ant Optimized Perron–Frobenius Eigen Centrality for Social Networks
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 op...
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MDPI AG
2023-08-01
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| 丛编: | ISPRS International Journal of Geo-Information |
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| 在线阅读: | https://www.mdpi.com/2220-9964/12/8/316 |
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| author | P.V. Kumaraguru Vidyavathi Kamalakkannan Gururaj H L Francesco Flammini Badria Sulaiman Alfurhood Rajesh Natarajan |
| author_facet | P.V. Kumaraguru Vidyavathi Kamalakkannan Gururaj H L Francesco Flammini Badria Sulaiman Alfurhood Rajesh Natarajan |
| author_sort | P.V. Kumaraguru |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T23:53:06Z |
| format | Article |
| id | doaj.art-20a4786db6814349aca9f0dde2d674b8 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T23:53:06Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-20a4786db6814349aca9f0dde2d674b82023-11-19T01:23:45ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-08-0112831610.3390/ijgi12080316Hessian Distributed Ant Optimized Perron–Frobenius Eigen Centrality for Social NetworksP.V. Kumaraguru0Vidyavathi Kamalakkannan1Gururaj H L2Francesco Flammini3Badria Sulaiman Alfurhood4Rajesh Natarajan5Department of MCA, Guru Nanak College (Autonomous), Velachery Main Road, Velachery, Chennai 600042, Tamilnadu, IndiaDepartment of Electronics and Communication Engineering, Selvam College of Technology, Namkkal 637003, Tamilnadu, IndiaDepartment of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 570064, Karnataka, IndiaIDSIA USI-SUPSI, University of Applied Sciences and Arts of Southern Switzerland, 6928 Manno, SwitzerlandDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi ArabiaInformation Technology Department, University of Technology and Applied Sciences-Shinas, Al-Aqr, Shinas 324, OmanTerabytes 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.https://www.mdpi.com/2220-9964/12/8/316big dataMutual Distributedmulti-AGENTHessian Optimization of Ant FrobeniusPerronEigen Vector Centrality |
| spellingShingle | P.V. Kumaraguru Vidyavathi Kamalakkannan Gururaj H L Francesco Flammini Badria Sulaiman Alfurhood Rajesh Natarajan Hessian Distributed Ant Optimized Perron–Frobenius Eigen Centrality for Social Networks ISPRS International Journal of Geo-Information big data Mutual Distributed multi-AGENT Hessian Optimization of Ant Frobenius Perron Eigen Vector Centrality |
| title | Hessian Distributed Ant Optimized Perron–Frobenius Eigen Centrality for Social Networks |
| title_full | Hessian Distributed Ant Optimized Perron–Frobenius Eigen Centrality for Social Networks |
| title_fullStr | Hessian Distributed Ant Optimized Perron–Frobenius Eigen Centrality for Social Networks |
| title_full_unstemmed | Hessian Distributed Ant Optimized Perron–Frobenius Eigen Centrality for Social Networks |
| title_short | Hessian Distributed Ant Optimized Perron–Frobenius Eigen Centrality for Social Networks |
| title_sort | hessian distributed ant optimized perron frobenius eigen centrality for social networks |
| topic | big data Mutual Distributed multi-AGENT Hessian Optimization of Ant Frobenius Perron Eigen Vector Centrality |
| url | https://www.mdpi.com/2220-9964/12/8/316 |
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