The Use of Collaboration Distance in Scheduling Conference Talks

Jan Pisanski, Tomaž Pisanski


Several bibliographic databases offer a free tool that enables one to determine the collaboration distance or co-authorship distance between researchers. This paper addresses a real-life application of the collaboration distance. It concerns somewhat unusual clustering; namely clustering in which the the average distances in each cluster need to be maximised. We briefly consider a pair of clusterings in which two cluster partitions are uniform and orthogonal in the sense that in each partition all clusters are of the same size and that no pair of elements belongs to the same cluster in both partitions. We consider different objective functions when calculating the score of the pair of orthogonal partitions. The one used on the Wiener index (a graph invariant, known in chemical graph theory) is used. The main application of our work is an algorithm for scheduling a series of parallel talks in a major conference.

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A.L Barabási and R Albert. Emergence of scaling in random networks. Science, vol. 286 (1999), no. 5439, pp. 509–512.

T. Bartol, K. Stopar, and G. Budimir. Visualization and knowledge discovery in metadata enriched aggregated data repositories harvesting from Scopus and Web of Science. Information management in the big data era: for a better world : Selected IMCW2015 Papers. Sun Yat-sen University North: Hacettepe University, 2015. pp 1–5.

T. Bartol, et al. Mapping and classification of agriculture in Web of Science: other subject categories and research fields may benefit. Scientometrics, vol. 109 (2016), no. 2, pp. 979–996.

V. Batagelj. On Fractional Approach to Analysis of Linked Networks, arxiv (2019)

V. Batagelj and A. Mrvar. Some analyses of Erdos˝collaboration graph. Social Networks, vol. 22 (2000),no. 2, pp. 173–186.

J.A. Bondy and U.S.R. Murty. Graph theory, (2008) Graduate Texts in Mathematics, 244. Springer, New York.

M. M. Deza and E. Deza. Encyclopedia of distances. Fourth edition. (2016), Springer, Berlin.

A.A. Dobrynin. On 2-connected transmission irregular graphs Diskretn. Anal. Issled. Oper., vol. 25 (2018), no. 4, pp. 5–14.

A. Ferligoj et al. Scientific collaboration dynamics in a national scientific system. Scientometrics, vol. 104 (2015), no. 3, pp. 985–1012.

C. Goffman. And what is your Erdos number?, ˝ Amer. Math. Monthly, vol. 76 (1979), p. 791.

L. Kronegger, F. Mali, A. Ferligoj, and P. Doreian. Collaboration structures in Slovenian scientific communities. Scientometrics, vol. 90 (2012), no.2, pp. 631–647.

J. Leskovec, A. Rajaraman, and J. Ullman. Mining of Massive Datasets (2014), Cambridge University Press.





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