Why a few Social Networking Sites Succeed While Many Fail

Kordestani, Arash; Limayem, Moez; Salehi‐Sangari, Esmail; Blomgren, Henrik; Afsharipour, Afshin

doi:10.1007/978-3-319-10873-5_164

Cited by 2 publications

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References 10 publications

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“…Recent studies by Malliaros et al [14] and Bhawalkar et al [15] provided theoretical models for understanding the social engagement in online social networks with a potential to predict social inactivity. While investigating the reasons behind the inactivity of the members of the online social networks is not in the scope of this work, some recent studies proposed some answers [16,17], suggesting that the main reason behind this decay is the inactivity of the members of the online social networks. Building a sound understanding of the decay dynamics of networks requires not only studying the static properties of these networks, but also requires investigating their behavior over time, and this is what we are interested in.…”

Section: Introductionmentioning

confidence: 99%

A Theoretical Model for Understanding the Dynamics of Online Social Networks Decay

Abufouda

2016

Preprint

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Online social networks represent a main source of communication and information exchange in today's life. They facilitate exquisitely news sharing, knowledge elicitation, and forming groups of same interests. Researchers in the last two decades studied the growth dynamics of the online social networks extensively questing a clear understanding of the behavior of humans in online social networks that helps in many directions, like engineering better recommendation systems and attracting new members. However, not all of social networks achieved the desired growth, for example, online social networks like MySpace, Orkut, and Friendster are out of service today. In this work, we present a probabilistic theoretical model that captures the dynamics of the social decay due to the inactivity of the members of a social network. The model is proved to have some interesting mathematical properties, namely submodularity, which imply achieving the model optimization in a reasonable performance. That means the maximization problem can be approximated within a factor of (1 − 1/e) and the minimization problem can be achieved in polynomial time.

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Section: Introductionmentioning

confidence: 99%