[Om-announce] Announcing the Encyclopedia of Social Network Analysis and Mining by Springer

[diaylosteiman]

Encyclopedia of Social Network Analysis and Mining

*Alhajj*, Reda, *Rokne*, Jon (Eds.)

2014, XLII, 2437 p. 611 illus., 425 illus. in color. In 3 volumes, not
available separately.

http://www.springer.com/computer/communication+networks/book/978-1-4614-6169-2


*About this encyclopedia*

   - Explains fundamental concepts of social networks and data mining
   across the disciplines in readable, authoritative entries
   - Addresses privacy, security, ethical, and civil liberty issues for
   social networks, and the application of social network methodologies to
   other *domains*
   - Includes methodologies for analysis of constructed networks, data
   mining techniques and *research* directions



The *Encyclopedia of Social Network Analysis and Mining* (ESNAM) is the
first major reference work to integrate fundamental concepts and research
directions in the areas of social networks and applications to data mining.
While ESNAM reflects the state-of-the-art in social network research, the
field had its start in the 1930s when fundamental issues in social network
research were broadly defined. These communities were limited to relatively
small numbers of nodes (actors) and links. More recently the advent of
electronic communication, and in particular on-line communities, have
created social networks of hitherto unimaginable sizes. People around the
world are directly or indirectly connected by popular social networks
established using web-based platforms rather than by physical proximity.

Reflecting the interdisciplinary nature of this unique field, the essential
contributions of diverse disciplines, from computer science, mathematics,
and statistics to sociology and behavioral science, are described among the
300 authoritative yet highly readable entries. Students will find a world
of information and insight behind the familiar façade of the social
networks in which they participate. Researchers and practitioners will
benefit from a comprehensive perspective on the methodologies for analysis
of constructed networks, and the data mining and machine learning
techniques that have proved attractive for sophisticated knowledge
discovery in complex applications. Also addressed is the application of
social network methodologies to other domains, such as web networks and
biological networks.
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