2011/05/25 by Salvatore Catanese, Salvatore A. Catanese, Pasquale De Meo +3 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · Social Sciences · #Centrality #Complex Network Analysis Techniques #Complex network #Computer science #Crawling #Data collection #Data science #Degree distribution #Friendship #Graph #Human Mobility and Location-Based Analysis #Internet privacy #Mathematics #Opinion Dynamics and Social Influence #Set (abstract data type) #Social graph #Social media #Social network (sociolinguistics) #Social network analysis #Sociology #Theoretical computer science #Web crawler #World Wide Web #acm:91D30 #cs.CY #cs.SI #msc:91D30 #physics.soc-ph
paper · pdf · doi:10.1145/1988688.1988749
published as Proceedings of the International Conference on Web Intelligence, Mining and Semantics, 2011 · WIMS '11: International Conference on Web Intelligence, Mining and Semantics ACM New York, NY, USA \c{opyright}2011 ISBN: 978-1-4503-0148-0
openalex publication_date 2011/05/25 · arxiv created 2011/05/31 · arxiv updated 2011/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.