Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/18981
Title: | The Central Community of Twitter ego-Networks as a Means for Fake Influencer Detection | Authors: | Tsapatsoulis, Nicolas Anastasopoulou, Vasiliki Ntalianis, Klimis S. |
Major Field of Science: | Natural Sciences | Field Category: | Computer and Information Sciences | Keywords: | Community detection;Degeneracy;Genetic algorithms;Graph partitioning;K-core;Social networks;Twitter ego networks | Issue Date: | 4-Nov-2019 | Source: | IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, Cyber Science and Technology Congress, 2019, 5-8 August, Fukuoka, Japan | Project: | EnhaNcing seCurity And privacy in the Social wEb: a user centered approach for the protection of minors | Conference: | IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, Cyber Science and Technology Congress | Abstract: | The central community of social networks, usually represented through the highest degree k-core of the corresponding graph, is proposed here as a compact representation of large social networks. We show that the central community of egocentric social media networks, such as the ego networks on Twitter and Instagram, tell us much more about the actual influence of the ego than the whole egocentric network itself. We also propose a novel genetic algorithm for the identification of central community of egocentric social networks and we examine the importance of the proper initialisation of this algorithm. The actual Twitter ego networks we used in our experiments along with the corresponding Python code are made publicly available for anyone who wishes to use them. | URI: | https://hdl.handle.net/20.500.14279/18981 | ISBN: | 978-1-7281-3024-8 | DOI: | 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00042 | Rights: | © IEEE | Type: | Conference Papers | Affiliation : | Cyprus University of Technology National and Kapodistrian University of Athens University of West Attica |
Publication Type: | Peer Reviewed |
Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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