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Analysis and mining of online social networks: emerging trends and challenges
Author(s) -
Bhat Sajid Yousuf,
Abulaish Muhammad
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1105
Subject(s) - popularity , data science , social network analysis , computer science , social network (sociolinguistics) , viral marketing , field (mathematics) , sentiment analysis , multidisciplinary approach , cluster analysis , node (physics) , organizational network analysis , scale (ratio) , social media , data mining , world wide web , knowledge management , artificial intelligence , sociology , social science , engineering , psychology , social psychology , organizational learning , mathematics , structural engineering , pure mathematics , physics , quantum mechanics
Social network analysis ( SNA ) is a multidisciplinary field dedicated to the analysis and modeling of relations and diffusion processes among various objects in nature and society, and other information/knowledge processing entities with an aim of understanding how the behavior of individuals and their interactions translates into large‐scale social phenomenon. Because of exploding popularity of online social networks and availability of huge amount of user‐generated content, there is a great opportunity to analyze social networks and their dynamics at resolutions and levels not seen before. This has resulted in a significant increase in research literature at the intersection of the computing and social sciences leading to several techniques for social network modeling and analysis in the area of machine learning and data mining. Some of the current challenges in the analysis of large‐scale social network data include social network modeling and representation, link mining, sentiment analysis, semantic SNA , information diffusion, viral marketing, and influential node mining. WIREs Data Mining Knowl Discov 2013, 3:408–444. doi: 10.1002/widm.1105 This article is categorized under: Algorithmic Development > Web Mining Commercial, Legal, and Ethical Issues > Social Considerations Technologies > Structure Discovery and Clustering