Publication Type

Editorial

Publication Date

10-2016

Abstract

The research on social networks has advanced significantly, which can be attributed to the prevalence of the online social websites and instant messaging systems as well as the popularity of mobile apps that support easy access to online social networks. These social networks are usually characterized by the complex network structures and rich contextual information. They now become the key platforms for, among others, content dissemination, professional networking, recommendation, alerting, and political campaigns. As online social network users perform activities on the social networks, they leave data traces of human behavior which allow the latter to be studied at scale. There are however a wide range of challenges in analyzing human behavior in social networks. Behavior analysis in online social networks spans a number of disciplines, across numerous fields in and beyond computer science. For example, one would have to involve social network analysis, an area in social science, to analyze social relationships, how they evolve and mature over time. The results of behavior analysis have important implications on community discovery, anomaly detection, and trend prediction, and they can enhance applications in multiple domains such as information retrieval, recommendation systems, and trust and security. Research in behavior analysis is a fertile ground also for businesses and IT industry, as they develop innovative ideas fostering the design of the new generation of social network platforms and their services.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

Neurocomputing

Volume

210

First Page

1

Last Page

2

ISSN

0925-2312

Identifier

10.1016/j.neucom.2016.06.008

Publisher

Elsevier

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1016/j.neucom.2016.06.008

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