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0 |
4 |
|a 006.754
|2 22
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| 100 |
1 |
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|a Tang, Lei,
|d 1982-
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| 245 |
1 |
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|a Community detection and mining in social media /
|c Lei Tang, Huan Liu
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| 260 |
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|a Cham, Switzerland :
|b Springer,
|c ©2010
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| 300 |
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|a 1 online resource (xi, 123 pages) :
|b illustrations
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| 336 |
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| 338 |
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| 490 |
1 |
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|a Synthesis lectures on data mining and knowledge discovery,
|x 2151-0075 ;
|v #3
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| 504 |
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|a Includes bibliographical references (pages 105-115) and index
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| 505 |
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|a Acknowledgments -- 1. Social media and social computing -- Social media -- Concepts and definitions -- Networks and representations -- Properties of large-scale networks -- Challenges -- Social computing tasks -- Network modeling -- Centrality analysis and influence modeling -- Community detection -- Classification and recommendation -- Privacy, spam and security -- Summary
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| 520 |
3 |
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|a The past decade has witnessed the emergence of participatory Web and social media, bringing people together in many creative ways. Millions of users are playing, tagging, working, and socializing online, demonstrating new forms of collaboration, communication, and intelligence that were hardly imaginable just a short time ago. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale. This lecture, from a data mining perspective, introduces characteristics of social media, reviews representative tasks of computing with social media, and illustrates associated challenges. It introduces basic concepts, presents state-of-the-art algorithms with easy-to-understand examples, and recommends effective evaluation methods. In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media. We also demonstrate how discovered patterns of communities can be used for social media mining. The concepts, algorithms, and methods presented in this lecture can help harness the power of social media and support building socially-intelligent systems. This book is an accessible introduction to the study of community detection and mining in social media. It is an essential reading for students, researchers, and practitioners in disciplines and applications where social media is a key source of data that piques our curiosity to understand, manage, innovate, and excel
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| 650 |
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|a Data mining
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|a Social media
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|a Exploration de données (Informatique)
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| 650 |
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6 |
|a Médias sociaux
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7 |
|a COMPUTERS
|x Web
|x Social Networking
|2 bisacsh
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|a Data mining
|2 fast
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|a Social media
|2 fast
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|a social media
|2 aat
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|a behavioral study
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| 653 |
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|a centrality analysis
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| 653 |
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|a collective classification
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| 653 |
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|a community detection
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| 653 |
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|a community evaluation
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| 653 |
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|a community evolution
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| 653 |
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|a correlation
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| 653 |
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|a heterogeneous networks
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|a homophily
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|a influence maximization
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|a influence modeling
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|a influence
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|a information diffusion
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|a multi-dimensional networks
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|a multi-mode networks
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|a social dimension
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| 653 |
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|a social media mining
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| 653 |
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|a social media
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| 653 |
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|a strength of ties
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| 700 |
1 |
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|a Liu, Huan,
|d 1958-
|
| 776 |
0 |
8 |
|i Print version:
|a Tang, Lei, 1982-
|t Community detection and mining in social media
|d [San Rafael, Calif.] : Morgan & Claypool Publishers, ©2010
|z 9781608453542
|w (OCoLC)693949785
|
| 830 |
|
0 |
|a Synthesis lectures on data mining and knowledge discovery ;
|v #3
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