Nov. 8, 2012 For the first time, the dynamics of how Facebook user communities are formed have been identified, revealing surprisingly few large communities and innumerable highly connected small-size communities.
These findings are about to be published in EPJ Data Science by Italian scientist Emilio Ferrara, affiliated with both Indiana University in Bloomington, Indiana, USA and his home University of Messina. This work could ultimately help identify the most efficient way to spread information, such as advertising, or ideas over large networks.
No previous work has attempted to analyse the community structure of Facebook as a proxy to understanding real world communities at the same scale.
The author elected to analyse Facebook with the mathematical tools typically used to study complex systems in order to uncover its dynamics. First, Ferrara acquired a snapshot of the structure of the users' friendship network using several techniques of statistical sampling applied to the anonymised public profiles of Facebook users. He then validated his approach to detect communities by comparing the outcome of several statistical methods and by using various algorithms.
He found that Facebook communities emerge as a result of the network's structure, which is based on creating networks of friends. It therefore has little to do with how individual users behave. Ferrara also realised that only few large communities emerge. Instead, users tend to aggregate in small-sized communities that are extremely interconnected. This type of structure is known to optimise the efficiency of communications among users. Indeed, short paths of communication can connect any pair of users, even if they belong to completely disparate communities.
Ultimately, this approach could be applied to verify a social theory known as Granovetter's "strength of weak ties," whereby loose interconnections among users yield better opportunities and more efficient communication channels.
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- Emilio Ferrara. A large-scale community structure analysis in Facebook. EPJ Data Science, 2012; 1 (1): 9 DOI: 10.1140/epjds9
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