Intelligent Node Influence Modeling Using Cascade Kronecker Neuro- Fuzzy Networks: A Computational Intelligence Framework for Dynamic Network Analysis

Kolli, Chandra Sekhar (Author)/ Vadisena, Venkata Krishna Reddy (Author)/ G, AKS Rajeev Kumar (Author)

ISBN 10: 9999329640 / ISBN 13: 9789999329644

Published by Eliva Press, 2025

About this Item

48 pages. 6.00x0.11x9.00 inches. In Stock.

Seller Inventory # x-9999329640

Bibliographic Details

• Title: Intelligent Node Influence Modeling Using Cascade Kronecker Neuro- Fuzzy Networks: A Computational Intelligence Framework for Dynamic Network Analysis
• Publisher: Eliva Press
• Publication Date: 2025
• Language: English
• ISBN 10: 9999329640
• ISBN 13: 9789999329644
• Binding: Paperback
• Condition: Brand New
• Weight: 0.08 kilograms

About this title

Synopsis

In an era of increasingly complex and dynamic networks, identifying influential nodes is crucial for controlling the spread of information, rumors, or epidemics. Intelligent Node Influence Modeling Using Cascade Kronecker Neuro-Fuzzy Networks introduces a powerful computational intelligence framework that combines Deep Kronecker Networks with Neuro-Fuzzy Logic in a cascade architecture to tackle the challenge of influential node detection and community analysis. This book presents the Cascade Kronecker Neuro-Fuzzy Network (Cascade KNFN)—a novel hybrid model developed to identify key nodes in large-scale networks with higher precision and lower computational cost. It leverages advanced centrality measures such as chord-distance-based betweenness, degree, and closeness to improve network influence prediction. The model’s strength lies in its ability to analyze both node-level influence and community-level dynamics, offering practical insights for domains such as social network analysis, epidemic modeling, communication networks, and cyber-physical systems. Key features: A comprehensive introduction to node influence modeling in complex networks. Novel application of Cascade KNFN for accurate community detection and merging. Evaluation based on betweenness and communication cost metrics. Applicable in real-world scenarios like rumor control and epidemic containment. Suitable for graduate students, researchers, and professionals in data science, computer science, and network theory. Whether you are a researcher looking to explore hybrid models or a student aiming to understand advanced network analytics, this book provides a solid foundation and practical toolkit for navigating the complexities of modern network systems.