CALL FOR CHAPTERS
“Exploratory Analysis in Dynamic Social Networks: theoretical and 
practical applications”

Submission Deadline: January 31, 2012
A book edited by Dr. Carlos Andre Reis Pinheiro, Analytics Lab, Oi 
Telecom, Brazil and Dr. Markus Helfert, School of Computing, Dublin City 
University, Ireland
To be published by iConcept Press: http://www.iconceptpress.com.

Introduction
Interactions within social networks have been subject of recent studies. 
As insights into these interactions offer great opportunities, many 
practical applications have been proposed. Recent approaches focus on 
using social network analysis’ methodology to study different aspects of 
societies, communities, knowledge networks and competitive markets, such 
as Social Medias through the Internet and Telecommunications 
environments. By understanding social structures and its interactions it 
is possible to understand how individuals and consumers relate to each 
other allowing to subsequently forecasts behavior and social structures.

Most of the current efforts in analyzing social networks are in respect 
to static structures. The social networks are considered by viewing 
snapshots of a specific point in time. However, analyzing dynamic 
aspects of social networks could provide insight how these structures 
evolve. The dynamic approach for social network analysis might provide 
insight into new perspectives in terms of pattern recognition, 
predictive and simulation of social structures. With new technologies, 
such as sensor data, analyses of movements and directions within social 
networks are also subject of recent studies viewing social networks 
dynamically.
In order to perceive how social relations evolve through time it is 
necessary to collect the distinct snapshots of the social structures in 
a timeline perspective. A sequence of pictures about social structures 
should be taken into consideration to allow particular analyses about 
how the members relate to each other but mostly how those relationships 
have been changed over time.
Measures typically used in traditional social networks analysis can be 
used such as characteristics of nodes and links in a static perspective, 
depicting strength, overall distances and paths among related nodes, 
amounts of connections, and others. The dynamic perspective on social 
networks provides a historical view on data for particular events; such 
as purchasing, acquisition, churn, fraud, and others; in a similar way 
presented in predictive modeling. Due to historical information about 
social structure and its movements throughout time, it is possible to 
analyze the network in terms of behavior, structure and topology.

Objective of the Book
This book aims to provide relevant theoretical frameworks and practical 
applications of innovative approaches to analyze social networks from a 
dynamic perspective. The key objective of this book is to reflect and 
document the current discussion about algorithms, metrics, mechanisms 
and particular applications and solutions in terms of dynamic analyses 
of social networks. Theories about new metrics to describe social 
structures over the time, considering nodes and links, its weights, 
distances and paths, and so on, are subject of discussion. Real world 
applications in respect to particular industries such as 
telecommunications, insurance, retail and mostly internet are also 
particularly welcome and encouraged.

Target Audience
The main goal of this book is to be a guide edition suitable for 
practitioners and researchers in the area of social network analysis, 
particular to the ones performed over time, considering the evolvement 
of social structures in a timeline perspective.

Recommended topics include, but are not limited to, the following:
* Fraud detection by using social network analysis
* Application of social network analysis and mining
* Communities discovery and analysis in large scale online and offline 
social networks
* Dynamics and evolution of patterns in social networks
* Geography applications for social networks analysis
* Impact of social networks in recommendations systems
* Large-scale graph algorithms for social network analysis
* Misbehavior detection in communities
* Migration between communities over the time
* Recommendations for product adoption, customer acquisition and churn 
prevention by using social network analysis
* Scalability of social networks
* Statistical modeling of large networks
* Temporal analysis on social networks topologies and structures
* Visual representation of dynamic social networks

Submission Procedure
Researchers and practitioners are invited to submit by January 31, 2012, 
a full chapter containing up to 12,000 words (around 20 pages). Authors 
of accepted draft chapters will be notified by April 15, 2012 about the 
status of their submission. All submitted chapters will be reviewed on a 
double-blind review basis. Contributors may also be requested to serve 
as reviewers for this project.
All submissions should be proceeded by registering an account through 
the iConcept Press’ website at www.iconceptpress.com.

Publisher
This book is scheduled to be published by iConcept Press Ltd. iConcept 
Press is a young publishing company established in the summer of 2009. 
Their main goals are:
* To open the door to the world's library of scientific knowledge by 
giving people anywhere in the world unlimited access to the latest 
scientific research.
* To facilitate research and education by making it possible to search 
the full text of every article in our library for free.
* To enable scientists, researchers, librarians, publishers, and 
entrepreneurs to develop innovative ways to explore and use the world's 
treasury of scientific ideas and discoveries.
For additional information regarding the publisher, please visit 
www.iconceptpress.com. The publication is targeted for early 2012.

Important Dates
January 31, 2012:	Draft Chapter Submission Deadline
April 15, 2012:		Notification of Acceptance
June 30, 2012:		Final Chapter Submission


Editorial Advisory Board Members
Dr. Carlos Andre Reis Pinheiro, Analytics Lab, Oi Telecom, Brazil.
Dr. Markus Helfert, School of Computing, Dublin City University, Ireland.

Inquiries and submissions can be forwarded electronically to:
Dr. Carlos Andre Reis Pinheiro (cpinheiro@computing.dcu.ie).
Dr. Markus Helfert (markus.helfert@computing.dcu.ie).



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