We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.
Crawling Facebook for social network analysis purposes / S.A. Catanese, P. De Meo, E. Ferrara, G. Fiumara, A. Provetti - In: WIMS '11: Proceedings[s.l] : ACM, 2011. - ISBN 9781450301480. - pp. 1-8 (( Intervento presentato al 1. convegno International Conference on Web Intelligence, Mining and Semantics tenutosi a Sogndal nel 2011 [10.1145/1988688.1988749].
Crawling Facebook for social network analysis purposes
A. ProvettiUltimo
2011
Abstract
We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.File | Dimensione | Formato | |
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