Scientific Social Networks: topological analysis of the influence of researchers

• Vitor Horta Federal University of Juiz de Fora
• Victor Ströele Federal University of Juiz de Fora https://orcid.org/0000-0001-6296-8605
• Fernanda Campos Federal University of Juiz de Fora
• José Maria N. David Federal University of Juiz de Fora
• Regina Braga Federal University of Juiz de Fora

DOI: https://doi.org/10.5753/sbbd.2017.166519

Abstract

Communities in social networks are composed by people with common interests who influence or are influenced by themselves. In this work, complex network analysis concepts are applied to verify the influence level among researchers, analyzing the structure of the scientific social network and its communities. We propose a bidirectional graph-based model to analyze the influence between researchers, and two algorithms to analyze the network structure, to identify scientific communities and to locate multidisciplinary researchers. To evaluate the model and the algorithms, a large scientific database is used in a use case. The results point to the solution viability and effectiveness.

Keywords: Communities in Social Networks, Graphs

References

Almeida, R., Pereira, C. K., Campos, F., and Stroele, V. (2016). Recomendação de recursos educacionais para grupos: buscando soluções em redes sociais. In Anais do XXVII Simpósio Brasileiro de Informática na Educação (SBIE 2016).

Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. pages 226–231. AAAI Press.

Falkowski, T., Barth, A., and Spiliopoulou, M. (2007). Dengraph: A density-based community detection algorithm. In In Proc. of the 2007 IEEE / WIC / ACM International Conference on Web Intelligence,, pages 112–115.

Gialampoukidis, I., Tsikrika, T., Vrochidis, S., and Kompatsiaris, I. (2016). Community detection in complex networks based on dbscan* and a martingale process. In 2016 11th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), pages 1–6.

Li, X., Tan, Y., Zhang, Z., and Tong, Q. (2016). Community detection in large social networks based on relationship density. In 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC). IEEE.

Lopes, G. R., Moro, M. M., Wives, L. K., and de Oliveira, J. P. M. (2010). Cooperative Authorship Social Network. In AMW.

Luo, T., Zhong, C., Ying, X., and Fu, J. (2011). Detecting community structure based on edge betweenness. In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE.

Ströele, V., Zimbrão, G., and Souza, J. M. (2013). Group and link analysis of multirelational scientific social networks. Journal of Systems and Software, 86:1819–1830.

Wasserman, S. and Faust, K. (1994). Social network analysis: Methods and applications, volume 8. Cambridge university press.