Multi-scale Dynamics in a Massive Online Social Network
Xiaohan Zhao
Alessandra Sala
Christo Wilson
Xiao Wang
Sabrina Gaito
Haitao Zheng
Ben Y. Zhao
Proceedings of The 12th ACM SIGCOMM Internet Measurement Conference (IMC 2012)
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Paper Abstract
Data confidentiality policies at major social network providers have
severely limited researchers' access to large-scale datasets. The
biggest impact has been on the study of network dynamics, where
researchers have studied citation graphs and content-sharing networks,
but few have analyzed detailed dynamics in the massive social networks
that dominate the web today. In this paper, we present results of
analyzing detailed dynamics in a large Chinese social network, covering
a period of 2 years when the network grew from its first user to 19
million users and 199 million edges. Rather than validate a single model
of network dynamics, we analyze dynamics at different granularities
(per-user, per-community, and network-wide) to determine how much, if
any, users are influenced by dynamics processes at different scales. We
observe independent predictable processes at each level, and find that
the growth of communities has moderate and sustained impact on users.
In contrast, we find that significant events such as network merge
events have a strong but short-lived impact on users, and they are
quickly eclipsed by the continuous arrival of new users.