Community structure discovery in Facebook. Emilio Ferrara International Journal of Social Network Mining, 1(1):67-90, 2012.
Community structure discovery in Facebook [link]Website  abstract   bibtex   
In this work we present a large-scale community structure detection and analysis of Facebook, which gathers more than 500 millions users at 2011. Characteristics of this social network have been widely investigated during the last years. Related works focus on analyzing its community structure on a small scale, usually from a qualitative perspective. In this study we consider a significant sample of the network. Data, acquired mining the Web platform, have been collected adopting two different sampling techniques. We investigated the structural properties of these samples in order to discover their community structure. Two well-known clustering algorithms, optimized for complex networks, have been here described and adopted. Results of our analysis show the emergence of a well-defined community structure inside Facebook, that is characterized by a power law distribution in the size of the communities. Moreover, the identified communities share an high degree of similarity, regardless the adopted detection algorithm.
@article{
 title = {Community structure discovery in Facebook},
 type = {article},
 year = {2012},
 keywords = {community detection,community structure,community structure analysis,community structure detection,complex networks,network clustering,network community analysis,social network analysis,social network clustrering,social network community analysis,social network community detection,social networks},
 pages = {67-90},
 volume = {1},
 websites = {http://www.inderscience.com/info/inarticle.php?artid=45106},
 id = {b83f4113-a131-3c1c-9ccf-58de81c0cd67},
 created = {2011-08-31T14:45:51.000Z},
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 last_modified = {2015-01-08T16:16:20.000Z},
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 abstract = {In this work we present a large-scale community structure detection and analysis of Facebook, which gathers more than 500 millions users at 2011.
Characteristics of this social network have been widely investigated during the last years.
Related works focus on analyzing its community structure on a small scale, usually from a qualitative perspective.
In this study we consider a significant sample of the network. 
Data, acquired mining the Web platform, have been collected adopting two different sampling techniques.
We investigated the structural properties of these samples in order to discover their community structure.
Two well-known clustering algorithms, optimized for complex networks, have been here described and adopted.
Results of our analysis show the emergence of a well-defined community structure inside Facebook, that is characterized by a power law distribution in the size of the communities.
Moreover, the identified communities share an high degree of similarity, regardless the adopted detection algorithm.
},
 bibtype = {article},
 author = {Emilio Ferrara, undefined},
 journal = {International Journal of Social Network Mining},
 number = {1}
}
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