Fuad Aleskerov,Sergey Shvydun,Natalia Meshcheryakova
New Centrality Measures in Networks: How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes
New Centrality Measures in Networks: How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes
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Excellent- More about New Centrality Measures in Networks: How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes
SLRIC is a software tool for analyzing complex networks that uses real-world case studies and Python models for centrality analysis. It is suitable for students and professional researchers.
\n Format: Hardback
\n Length: 102 pages
\n Publication date: 07 December 2021
\n Publisher: Taylor & Francis Ltd
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The SLRIC (Spatial Localization and Relational Inference for Complex Networks) toolkit is a powerful and versatile tool for analyzing complex networks. It is built around real-world case studies in a wide range of fields, including finance, migration, trade, and more. Whether you are a student or a professional researcher interested in complex network analysis, the SLRIC toolkit is designed to help you explore and understand the intricate relationships within these networks.
One of the key features of the SLRIC toolkit is its suitability for both students and professional researchers. The toolkit is designed to be easy to use and accessible, making it accessible to a wide range of users. It includes a comprehensive set of documentation and tutorials that can help you get started with the toolkit and guide you through the process of analyzing your data.
In addition to its user-friendly interface, the SLRIC toolkit is also paired with a software package for readers who wish to apply the proposed models of centrality. The software package, available at https://github.com/SergSHV/slric, provides a range of tools and functions for analyzing network data, including measures of centrality such as degree centrality, betweenness centrality, and eigenvector centrality.
The SLRIC toolkit is a valuable resource for anyone interested in complex network analysis. Whether you are a student, a professional researcher, or simply someone who is curious about the inner workings of complex systems, the toolkit provides you with the tools and knowledge you need to explore and understand these networks.
The SLRIC toolkit is a powerful and versatile tool for analyzing complex networks. It is built around real-world case studies in a wide range of fields, including finance, migration, trade, and more. Whether you are a student or a professional researcher interested in complex network analysis, the SLRIC toolkit is designed to help you explore and understand the intricate relationships within these networks.
One of the key features of the SLRIC toolkit is its suitability for both students and professional researchers. The toolkit is designed to be easy to use and accessible, making it accessible to a wide range of users. It includes a comprehensive set of documentation and tutorials that can help you get started with the toolkit and guide you through the process of analyzing your data.
In addition to its user-friendly interface, the SLRIC toolkit is also paired with a software package for readers who wish to apply the proposed models of centrality. The software package, available at https://github.com/SergSHV/slric, provides a range of tools and functions for analyzing network data, including measures of centrality such as degree centrality, betweenness centrality, and eigenvector centrality.
The SLRIC toolkit is a valuable resource for anyone interested in complex network analysis. Whether you are a student, a professional researcher, or simply someone who is curious about the inner workings of complex systems, the toolkit provides you with the tools and knowledge you need to explore and understand these networks.
The SLRIC toolkit is a powerful and versatile tool for analyzing complex networks. It is built around real-world case studies in a wide range of fields, including finance, migration, trade, and more. Whether you are a student or a professional researcher interested in complex network analysis, the SLRIC toolkit is designed to help you explore and understand the intricate relationships within these networks.
One of the key features of the SLRIC toolkit is its suitability for both students and professional researchers. The toolkit is designed to be easy to use and accessible, making it accessible to a wide range of users. It includes a comprehensive set of documentation and tutorials that can help you get started with the toolkit and guide you through the process of analyzing your data.
In addition to its user-friendly interface, the SLRIC toolkit is also paired with a software package for readers who wish to apply the proposed models of centrality. The software package, available at https://github.com/SergSHV/slric, provides a range of tools and functions for analyzing network data, including measures of centrality such as degree centrality, betweenness centrality, and eigenvector centrality.
The SLRIC toolkit is a valuable resource for anyone interested in complex network analysis. Whether you are a student, a professional researcher, or simply someone who is curious about the inner workings of complex systems, the toolkit provides you with the tools and knowledge you need to explore and understand these networks.
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Dimension: 216 x 138 (mm)\n
ISBN-13: 9781032063195\n \n
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