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Dynamics in Online Social Networks

  • Przemyslaw A. Grabowicz
  • José J. Ramasco
  • Víctor M. Eguíluz
Chapter
Part of the Modeling and Simulation in Science, Engineering and Technology book series (MSSET)

Abstract

An increasing number of today’s social interactions occur using online social media as communication channels. Some online social networks have become extremely popular in the last decade. They differ among themselves in the character of the service they provide to online users. For instance, Facebook can be seen mainly as a platform for keeping in touch with close friends and relatives, Twitter is used to propagate and receive news, LinkedIn facilitates the maintenance of professional contacts, and Flickr gathers amateurs and professionals of photography. Albeit different, all these online platforms share an ingredient that pervades all their applications. There exists an underlying social network that allows their users to keep in touch with each other and helps to engage them in common activities or interactions leading to a better fulfillment of the service’s purposes. This is the reason why these platforms share a good number of functionalities, e.g., personal communication channels, broadcasted status updates, easy one-step information sharing, and news feeds exposing broadcasted content. As a result, online social networks are an interesting field to study an online social behavior that seems to be generic among the different online services. Since at the bottom of these services lays a network of declared relations and the basic interactions in these platforms tend to be pairwise, a natural methodology for studying these systems is provided by network science. In this chapter, we describe some of the results of research studies on the structure, dynamics, and social activity in online social networks. We present them in the interdisciplinary context of network science, sociological studies, and computer science.

Keywords

Social Network Degree Distribution Cluster Coefficient Online Social Network Preferential Attachment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

We acknowledge partial support from the European Commission through PATRES project, the Spanish Ministry of Economy (MINECO) and FEDER (EU) through projects FISICOS (FIS2007-60327) and MODASS (FIS2011-247852). P. A. G. acknowledges support from the JAE program of the CSIC; J. J. R. acknowledges support from the Ramón y Cajal program of MINECO.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Przemyslaw A. Grabowicz
    • 1
  • José J. Ramasco
    • 1
  • Víctor M. Eguíluz
    • 1
  1. 1.Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB)Palma de MallorcaSpain

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