Node-centric Community Discovery: From static to dynamic social network analysis

2017 ◽  
Vol 3-4 ◽  
pp. 32-48 ◽  
Author(s):  
Giulio Rossetti ◽  
Dino Pedreschi ◽  
Fosca Giannotti
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori

This Chapter overviews most recent data mining approaches proposed in the context of social network analysis. In particular, it aims at classifying the proposed approaches based on both the adopted mining strategies and their suitability for supporting knowledge discovery in a dynamic context. To provide a thorough insight into the proposed approaches, main work issues and prospects in dynamic social network analysis are also outlined.


2014 ◽  
Vol 03 (01) ◽  
pp. 9-18 ◽  
Author(s):  
Sho Tsugawa ◽  
Hiroyuki Ohsaki ◽  
Yuichi Itoh ◽  
Naoaki Ono ◽  
Keiichiro Kagawa ◽  
...  

Author(s):  
Preeti Gupta ◽  
Vishal Bhatnagar

The social network analysis is of significant interest in various application domains due to its inherent richness. Social network analysis like any other data analysis is limited by the quality and quantity of data and for which data preprocessing plays the key role. Before the discovery of useful information or pattern from the social network data set, the original data set must be converted to a suitable format. In this chapter we present various phases of social network data preprocessing. In this context, the authors discuss various challenges in each phase. The goal of this chapter is to illustrate the importance of data preprocessing for social network analysis.


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