Social Network Data Analytics (Paperback)
  • Social Network Data Analytics (Paperback)

Social Network Data Analytics (Paperback)

Paperback 502 Pages / Published: 01/10/2014
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Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes.

Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book.

This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

Publisher: Springer-Verlag New York Inc.
ISBN: 9781489988935
Number of pages: 502
Weight: 783 g
Dimensions: 235 x 155 x 26 mm
Edition: 2011 ed.


From the reviews:

"Provides a comprehensive compendium of the state of the art in social network data mining. ... a very interesting book for computer science researchers and practitioners who work in the area of data mining and want to learn the state of the art in social network data analytics. The book provides good coverage of the subject area by focusing on the most popular research topics, and offers numerous bibliographic references that will guide readers who are interested in particular topics. I highly recommend this book." (Aris Gkoulalas-Divanis, ACM Computing Reviews, August, 2011)

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