Community detection in Social Media

By Symeon Papadopoulos, Yiannis Kompatsiaris, Athena Vakali, Ploutarchos Spyridonos. 2012

621 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

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Citations in the corpus

Debiasing Community Detection: The Importance of Lowly Connected Nodes (2019, methodology): “ommunity detection methods with low recall scores can suffer from incorporating bias by excluding so many significant users. Although there are many different methods for the community detection task [9, 17], we divided our analysis in two different sections: (1) Methods that do not use node attribu”
Negative Link Prediction in Social Media (2014, background): “…availability of large-scale online social network data is useful not only for various tasks in social network analysis such as community detection [20] and link prediction [15], but it is also leveraged for various traditional data mining tasks such as feature selection [22] and recommendations…”
Detecting cohesive and 2-mode communities indirected and undirected networks (2014, background): “Detecting communities of densely connected sets of nodes is an extensively researched area [14, 30, 35, 42] with a plethora of different algorithms and heuristics.”
A Survey of Signed Network Mining in Social Media (2015, methodology): “In unsigned networks, community detection identifies groups of densely connected nodes [Tang and Liu 2010; Papadopoulos et al. 2012; Ailon et al. 2013].”
A Comparative Analysis of Community Detection Algorithms on Artificial Networks (2016, background): “A previous study showed 24 that (theoretically) many community detection methods have minimum memory consumption needs that scale linearly with the size of the graph O ( 2 m + 2 n ) , where m is the number of edges and n is the number of nodes.”
Signed Network Embedding in Social Media (2017, methodology): “…analysis ; and network embedding, which aims to learn lowdimensional vector representations for nodes, has been proven to be useful in many tasks of social network analysis such as link prediction [1], community detection [2], node classification/clustering [3] [4] [5] and visualization [6].”