2016/06/16 by Youcef Abdelsadek, Kamel Chelghoum, Abdelsadek, Youcef +7
Computer Science · Physics and Astronomy · Psychology · #Advanced Clustering Algorithms Research #Complex Network Analysis Techniques #Data Visualization and Analytics #FOS: Computer and information sciences #FOS: Physical sciences #Mental Health Research Topics #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1606.05136
openalex publication_date 2016/06/16 · openalex created_date 2022/09/15 · openalex updated_date 2026/07/28
Nowadays, the interest given by the scientific community to the investigation\nof the data generated by social networks is increasing as much as the\nexponential increasing of social network data. The data structure complexity is\none among the snags, which slowdown their understanding. On the other hand,\ncommunity detection in social networks helps the analyzers to reveal the\nstructure and the underlying semantic within communities. In this paper we\npropose an interactive visualization approach relying on our application\nNLCOMS, which uses synchronous and related views for graph and community\nvisualization. Additionally, we present our algorithm for community detection\nin networks. A computation study is conducted on instances generated with the\nLFR [9]-[10] benchmark. Finally, in order to assess our approach on real-world\ndata, we consider the data of the ANR-Info-RSN project. The latter addresses\ncommunity detection in Twitter.\n