Analysis of methods of construction of the graph of co-authorship: an approach based on bipartite graph

Fedor Krasnov

Abstract


The current practice of design and implementation of co-authorship graphs implies the use of mathematical apparatus of graph theory. Traditionally, to build co-authorship graphs using undirected graphs. The authors of this study analysed an approach of bipartite directed graph as a tool for constructing graphs of co-authorship. The study shows the benefits of using a bipartite graph and a quantitative comparison of the traditional way of constructing the graph of co-authorship and method based on a bipartite graph using the centrality metrics of the graph.


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References


Mullins N. C. The development of specialties in social science: The case of ethnomethodology //Science Studies. – 1973. – T. 3. – #. 3. – S. 245-273.

Chuan P. M. et al. Link prediction in co-authorship networks based on hybrid content similarity metric //Applied Intelligence. – S. 1-17.

Chen Y. et al. Building and Analyzing a Global Co-Authorship Network Using Google Scholar Data //Proceedings of the 26th International Conference on World Wide Web Companion. – International World Wide Web Conferences Steering Committee, 2017. – S. 1219-1224.

Wei F. et al. A co-authorship network-based method for understanding the evolution of a research area: A case of information systems research //Malaysian Journal of Library & Information Science. – 2017. – T. 22. – #. 2. – S. 1-14.

Leifeld P. et al. Collaboration patterns in the German political science co-authorship network //PloS one. – 2017. – T. 12. – #. 4. – S. e0174671.

Ahmed T. et al. Analysis of co-authorship in computer networks using centrality measures //Communication, Computing and Digital Systems (C-CODE), International Conference on. – IEEE, 2017. – S. 54-57.

Chang H. J., Wang W. M. The Hidden Power of Social-Linkage in the Office: A Co-authorship Network Analysis //Proceedings of the 4th Multidisciplinary International Social Networks Conference on ZZZ. – ACM, 2017. – S. 4.

Köseoglu M. A. et al. Authorship trends, collaboration patterns, and co-authorship networks in lodging studies (1990–2016) //Journal of Hospitality Marketing & Management. – 2017. – #. just-accepted.

Paraschiv I. C. et al. Semantic Similarity versus Co-authorship Networks: A Detailed Comparison //Control Systems and Computer Science (CSCS), 2017 21st International Conference on. – IEEE, 2017. – S. 566-570.

Ho T. M. et al. with basic network measures of 2008-2017 Scopus data [version. – 2017.

Liu X. et al. Co-authorship networks in the digital library research community //Information processing & management. – 2005. – T. 41. – #. 6. – S. 1462-1480.

Wei F. et al. A co-authorship network-based method for understanding the evolution of a research area: A case of information systems research //Malaysian Journal of Library & Information Science. – 2017. – T. 22. – #. 2. – S. 1-14.

Zhang D. et al. Co-authorship Networks in Additive Manufacturing Studies Based on Social Network Analysis //British Journal of Applied Science & Technology. – 2016. – T. 15. – #. 1. – S. 1.

Gielfi G. G. et al. University-industry research collaboration in the Brazilian oil industry: the case of Petrobras //Rev. Bras. Inov. – 2017. – T. 16. – #. 2. – S. 325-350.

Krasnov F., Yavorskiy R. Measurement of maturity level of a professional community. – 2013.

Dokuka S., Yavorskiy R., Krasnov F. The Structure of Organization: the Coauthorship Network Case //Analysis of Images, Social Networks and Texts. 5th International Conference, AIST 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected Papers. Communications in Computer and Information Science. – Springer International Publishing, 2017. – S. 93-101.

Guimera R. et al. Team assembly mechanisms determine collaboration network structure and team performance //Science. – 2005. – T. 308. – #. 5722. – S. 697-702.

Krasnov F.V. Model' processa publikacij nauchno-prakticheskih statej po special'nosti 25.00 «Nauki o Zemle» // Internet-zhurnal «NAUKOVEDENIE» Tom 9, #5 (2017) https://naukovedenie.ru/PDF/62TVN517.pdf (dostup svobodnyj). Zagl. s jekrana. Jaz. rus., angl.

Prell C. Social network analysis: History, theory and methodology. – Sage, 2012.

Brandes U. A faster algorithm for betweenness centrality //Journal of mathematical sociology. – 2001. – T. 25. – #. 2. – S. 163-177.

Hasanov, Mars Magnavievich // Vikipedija. [2017—2017]. Data obnovlenija: 16.07.2017. URL: http://ru.wikipedia.org/?oldid=86557588 (data obrashhenija: 16.07.2017).

Freeman L. C. Centrality in social networks conceptual clarification //Social networks. – 1978. – T. 1. – #. 3. – S. 215-239.

Wasserman S., Faust K. Social network analysis: Methods and applications. – Cambridge university press, 1994. – T. 8.

Nistér D. Preemptive RANSAC for live structure and motion estimation //Machine Vision and Applications. – 2005. – T. 16. – #. 5. – S. 321-329.

Barabási A. L., Albert R. Emergence of scaling in random networks //science. – 1999. – T. 286. – #. 5439. – S. 509-512.

Eom Y. H., Fortunato S. Characterizing and modeling citation dynamics //PloS one. – 2011. – T. 6. – #. 9. – S. e24926.


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