Social networking:

Contributor(s):

Material type: Text

TextSeries: Intelligent systems reference library ; v. 65Publication details: New York: Springer, 2014Description: xxii, 295pISBN:

9783319051635

Subject(s):

DDC classification:

302.231 SOC

Summary: 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.

Item type:

Book

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode
Book	IIM Kashipur	302.231 SOC (Browse shelf(Opens below))	Available		6314

Includes bibliographical references and index.

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.

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