Exploiting Community Detection to Recommend Privacy Policies in Decentralized Online Social Networks

Author(s):  
Andrea De Salve ◽  
Barbara Guidi ◽  
Andrea Michienzi
Author(s):  
Anwitaman Datta ◽  
Sonja Buchegger ◽  
Le-Hung Vu ◽  
Thorsten Strufe ◽  
Krzysztof Rzadca

2020 ◽  
Vol 7 (1) ◽  
pp. 151-163 ◽  
Author(s):  
Tianxi Ji ◽  
Changqing Luo ◽  
Yifan Guo ◽  
Qianlong Wang ◽  
Lixing Yu ◽  
...  

2017 ◽  
Vol 01 (01) ◽  
pp. 1630001 ◽  
Author(s):  
Hossein Fani ◽  
Ebrahim Bagheri

Online social networks have become a fundamental part of the global online experience. They facilitate different modes of communication and social interactions, enabling individuals to play social roles that they regularly undertake in real social settings. In spite of the heterogeneity of the users and interactions, these networks exhibit common properties. For instance, individuals tend to associate with others who share similar interests, a tendency often known as homophily, leading to the formation of communities. This entry aims to provide an overview of the definitions for an online community and review different community detection methods in social networks. Finding communities are beneficial since they provide summarization of network structure, highlighting the main properties of the network. Moreover, it has applications in sociology, biology, marketing and computer science which help scientists identify and extract actionable insight.


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