Temporal Interaction Biased Community Detection in Social Networks
| Autor: | Noha Alduaiji, Xiaolu Lu, Amitava Datta, Wei Liu, Jianxin Li |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
Perspective (graphical) 02 engineering and technology Static structure 01 natural sciences Data science 010104 statistics & probability Fixed time Order (exchange) 020204 information systems 0202 electrical engineering electronic engineering information engineering Social relationship Social media 0101 mathematics Social data analytics |
| Zdroj: | Advanced Data Mining and Applications ISBN: 9783319495859 ADMA |
| DOI: | 10.1007/978-3-319-49586-6_27 |
| Popis: | Community detection in social media is a fundamental problem in social data analytics in order to understand user relationships and improve social recommendations. Although the problem has been extensively investigated, most of the research examined communities based on static structure in social networks. Our findings within large social networks such as Twitter, show that only a few users have interactions or communications within any fixed time interval. It is not difficult to see that it makes more potential sense to find such active communities that are biased to temporal interactions of social users, rather than relying solely on static structure. Communities detected with this new perspective will provide time-variant social relationships or recommendations in social networks, which can greatly improve the applicability of social data analytics. |
| Databáze: | OpenAIRE |
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