The first paper, titled “The influence of social status and network structure on consensus building in collaboration networks” was published as OpenAccess version and also as journal article in the December 2016 Issue of the journal Social Network Analysis and Mining (SNAM). The paper analyses the influence of social status on opinion dynamics and consensus building in collaboration networks and concludes that, according to the conducted research, the social status favours consensus building in most of the networks.
The second paper with the title “Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning” has been published in the Journal of Web Science (JWS). The authors investigate SUSTAIN, an unsupervised human category learning model that aims to mimic a learner’s categorization behaviour, using three social bookmarking datasets. Results show that SUSTAIN can indeed be used to help enrich and improve Collaborative Filtering for resource recommendations.
We congratulate all the authors to their success!
Bibliography of both papers:
I. Hasani-Mavriqi, F. Geigl, S. C. Pujari, E. Lex, and D. Helic. The influence of social status and network structure on consensus building in collaboration networks. Social Network Analysis and Mining, 6(1):1-17, 2016. doi:10.1007/s13278-016-0389-y
Simone Kopeinik, Dominik Kowald, Ilire Hasani-Mavriqi and Elisabeth Lex (2016), “Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning”. The Journal of Web Science, Vol 2