Centrality in heterogeneous social networks for lurkers detection: An approach based on hypergraphs. (5th June 2017)

• Record Type: Journal Article
• Authors: Amato, Flora; Moscato, Vincenzo; Picariello, Antonio; Piccialli, Francesco; Sperlí, Giancarlo
• Other Names: Choi Chang guestEditor.; Chen Hsing‐Chung guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 3(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-06-05
• Subjects: centrality measures -- hypergraph -- lurkers detection -- social networks analysis; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4188
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Nowadays, social networks provide users an interactive platform to create and share heterogeneous content for a lot of different purposes (eg, to comment events and facts, and express and share personal opinions on specific topics), allowing millions of individuals to create online profiles and share personal information with vast networks known and sometimes also unknown people. Knowledge about users, content, and relationships in a social network may be used for an adversary attack of some victims easily. Although a number of works have been done for data privacy preservation on relational data, they cannot be applied in social networks and in general for big data analytics. In this paper, we first propose a novel data model that integrates and combines information on users belonging to 1 or more heterogeneous online social networks, together with the content that is generated, shared, and used within the related environments, using an hypergraph data structure; then we implemented the most diffused centrality measures and also introduced a new centrality measure—based on the concept of "neighborhood" among users—that may be efficiently applied for a number of data privacy issues, such as lurkers and neighborhood attack prevention, especially in "interest‐based" social networks. Some experiments using the Yelp dataset are discussed.