Intelligent Node Influence Modeling Using Cascade Kronecker Neuro- Fuzzy Networks: A Computational Intelligence Framework for Dynamic Network Analysis - Softcover
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.
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Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9789999329644
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Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9789999329644
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Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9789999329644
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9789999329644
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Taschenbuch. Condition: Neu. Intelligent Node Influence Modeling Using Cascade Kronecker Neuro- Fuzzy Networks | Chandra Sekhar Kolli | Taschenbuch | Englisch | 2025 | Eliva Press | EAN 9789999329644 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 134364122
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