Dynamic social network mining: Issues and prospects

Luca Cagliero, Alessandro Fiori

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The past few years have witnessed the rapid proliferation of Web communities such as social networking sites, wikis, blogs, and media sharing communities. The published social content is commonly characterized by a high dynamicity and reflects the most recent trends and common user behaviors. The Data mining and Knowledge Discovery (KDD) process focuses on discovering and analyzing relevant information hidden in large data collections to support expert decision making. Hence, the application of data mining techniques to data coming from social networks and online communities is definitely an appealing research topic. 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.

Original languageEnglish
Title of host publicationData Mining in Dynamic Social Networks and Fuzzy Systems
PublisherIGI Global
Pages122-144
Number of pages23
ISBN (Print)9781466642140, 9781466642133
DOIs
Publication statusPublished - Jun 30 2013

ASJC Scopus subject areas

  • Computer Science(all)

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