Titlebook: Social Networking; Mining, Visualizatio Mrutyunjaya Panda,Satchidananda Dehuri,Gi-Nam Wang Book 2014 Springer International Publishing Swit

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Machine Learning for Auspicious Social Network Mining, learning for network data preparation and different learning techniques for descriptive and predictive analysis. Finally we have presented some machine learning based findings in the area of community detection, prediction, spatial-temporal and fuzzy analysis.

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Social Network Analysis: A Methodology for Studying Terrorism, and structures within the network. The methodology is illustrated by reviewing two case studies: the 9/11 terrorist network study by Krebs, that used data from a single terrorist attack, and a study by Basu that used data from about 200 terrorist incidents in India to create a network of terrorist organizations for predictive purposes.

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Testing Community Detection Algorithms: A Closer Look at Datasets,lled communities or clusters. Detecting such groups in a social network (i.e., community detection) remains a core problem in social network analysis. Among the challenges that face the researchers to come up with advanced community detection methods, there is a key challenge, which is the validatio

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Societal Networks: The Networks of Dynamics of Interpersonal Associations,ns. However, the inclusion of time variations made the social networks dynamic. In the present day scenario the social networks are more dynamic than static. The introduction of societal networks by J. Fiksel is an evolutionary step in the study of social networks which originated the concept of dyn

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