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Extraction and Analysis of Facebook Friendship Relations

Catanese, Salvatore and De Meo, Pasquale and Ferrara, Emilio and Fiumara, Giacomo and Provetti, Alessandro (2011) Extraction and Analysis of Facebook Friendship Relations. [Book Chapter]

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Abstract

Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem). However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms.

Item Type:Book Chapter
Subjects:Computer Science > Dynamical Systems
Computer Science > Machine Learning
ID Code:7668
Deposited By: Ferrara, Dr. Emilio
Deposited On:27 Oct 2011 01:34
Last Modified:27 Oct 2011 01:34

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