Multi-scale Dynamics in a Massive Online Social Network
arXiv:1205.4013
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 the Renren social network, covering a period of 2 years when the network grew from 1 user to 19 million users and 199 million edges. Rather than validate a single model of network dynamics, we analyze dynamics at different granularities (user-, community- and network- wide) to determine how much, if any, users are influenced by dynamics processes at different scales. We observe in- dependent predictable processes at each level, and find that while the growth of communities has moderate and sustained impact on users, significant events such as network merge events have a strong but short-lived impact that is quickly dominated by the continuous arrival of new users.
References in corpus (8)
- Emergence of scaling in random networks
- Fast unfolding of communities in large networks
- Finding and evaluating community structure in networks
- Finding community structure in very large networks
- Cooperative Game Theory Approaches for Network Partitioning
- Quantifying social group evolution
- Clique percolation in random networks
- Finding Community Structure in Mega-scale Social Networks
Cited by in corpus (5)
- Ego Network Structure in Online Social Networks and its Impact on Information Diffusion
- Evolution of Social-Attribute Networks: Measurements, Modeling, and Implications using Google+
- Google+ or Google-?: Dissecting the Evolution of the New OSN in its First Year
- A generalized theory of preferential linking
- Understanding Service Integration of Online Social Networks: A Data-Driven Study