Abstract
Nowadays, a lot of scientists’ works aim to improve the quality of people’s life but it could be quite complicated without building a successful collaboration. Productive partnerships can increase research efficiency in many cases and make a huge impact on society. However, today there is no clear way to find such collaborators. In this paper, we propose a recommender system for the scientists from the Higher School of Economics university to help them find co-authors for their prospective studies.
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Acknowledgements
Sections 2–5 were prepared under the support by the Russian Science Foundation under grant 17-11-01294, performed at National Research University Higher School of Economics, Russia. Section 1 was prepared under support by RFBR grant 16-29-09583 ‘Development of methodology, methods and tools for identifying and countering the proliferation of malicious information campaigns in the Internet’.
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7 Appendix
7 Appendix
In this section, the distribution of parameters from the models are given.
It is interesting that here exist a hump in the region of the number 800.
As it was expected, the highest hills are seen in the regions of the integer values of the variable. Moreover, there is a plenty of observations having exactly a score of 5 , which means that the journals where there were published are not good enough nowadays.
An interesting plot could be seen below. Of course, it could be expected that there is a large number of authors that have their journals only in low-citing journals. However, there are two bars not at the ends of the graph, which is quite interesting.
It can be seen from the graph that this distribution correlates with the year distribution shown in the section of the dataset description.
This graph shows that there is only a small amount of authors that have only papers with no topic association, which is very good. Moreover, as most of the previous graphs it looks as a power-law graph.
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Averchenkova, A. et al. (2020). Collaborator Recommender System. In: Bychkov, I., Kalyagin, V., Pardalos, P., Prokopyev, O. (eds) Network Algorithms, Data Mining, and Applications. NET 2018. Springer Proceedings in Mathematics & Statistics, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-030-37157-9_7
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