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Biblioteca Julio Castiñeiras. Sistema de Información Integrado - Facultad de Ingeniería UNLP
Facultad de Ingeniería | 115 esq.47 | Horario: Lunes a Viernes 8 a 19 hs.
E-mail: bibcentral@ing.unlp.edu.ar
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Información bibliografica (registro INGC-EBK-000448) |
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Título: |
Social Networking Mining, Visualization, and Security / edited by Mrutyunjaya Panda, Satchidananda Dehuri, Gi-Nam Wang. |
Otros autores: |
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Wang, Gi-Nam, ed.
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Dehuri, Satchidananda, ed.
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Panda, Mrutyunjaya, ed.
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Editado por: |
Springer International Publishing :;Imprint: Springer,
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Año de publicación: |
2014.
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Lugar de publicación: |
Cham :
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Descripción física: |
xxii, 297 p. : il. |
ISBN: |
9783319051642
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Colección: |
Intelligent Systems Reference Library,
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Materias: |
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Information Systems Applications (incl. Internet). |
Artificial Intelligence (incl. Robotics). |
Computational intelligence. |
Engineering. |
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Notas: |
Diffusion of Information in Social Networks -- Structure and Evolution of Online Social Networks -- Machine Learning for Auspicious Social Network Mining -- Testing Community Detection Algorithms: A Closer Look at Datasets -- Societal Networks: The networks of Dynamics of Interpersonal Associations -- Methods of tracking online community in social network -- Social Network Analysis Approach for Studying Caste, Class and Social Support in Rural Jharkhand and West Bengal: An Empirical Attempt -- Evaluating the Propagation Strength of Malicious Metaphor in Social Network: Flow Through Inspiring Influence of Members -- Social Network Analysis: A methodology for studying Terrorism -- Privacy and Anonymization in Social Networks -- On the use of Brokerage Approach to discover Influencing Nodes in Terrorist Networks. |
Sumario: |
With the proliferation of social media and on-line communities in networked world a large gamut of data has been collected and stored in databases. The rate at which such data is stored is growing at a phenomenal rate and pushing the classical methods of data analysis to their limits. This book presents an integrated framework of recent empirical and theoretical research on social network analysis based on a wide range of techniques from various disciplines like data mining, social sciences, mathematics, statistics, physics, network science, machine learning with visualization techniques, and security. The book illustrates the potential of multi-disciplinary techniques in various real life problems and intends to motivate researchers in social network analysis to design more effective tools by integrating swarm intelligence and data mining. Â . |
URL: |
http://dx.doi.org/10.1007/978-3-319-05164-2
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Tapa y contenido (Amazon.com) |
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