Collaboration graph

In mathematics and social science, a collaboration graph^[1][2] is a graph modeling some social network where the vertices represent participants of that network (usually individual people) and where two distinct participants are joined by an edge whenever there is a collaborative relationship between them of a particular kind. Collaboration graphs are used to measure the closeness of collaborative relationships between the participants of the network.

Types of collaboration graphs considered in the literature

The two most well-studied collaboration graphs are:

• Collaboration graph of mathematicians also known as the Erdős collaboration graph,^[3][4] where two mathematicians are joined by an edge whenever they co-authored a paper together (with possibly other co-authors present).
• Collaboration graph of movie actors, also known as the Hollywood graph,^[5][6][7] where two movie actors are joined by an edge whenever they appeared in a movie together.

Collaborations graphs have also been considered in other social networks, such as sports, including the "NBA graph" whose vertices are players where two players are joined by an edge if they have ever played together on the same team^[8]

Features of collaboration graphs

By construction, the collaboration graph is a simple graph, since it has no loop-edges and no multiple edges. The collaboration graph need not be connected. Thus people who never co-authored a joint paper represent isolated vertices in the collaboration graph of mathematicians.

Both the collaboration graph of mathematicians and movie actors were shown to have "small world topology": they have a very large number of vertices, most of small degree, that are highly clustered, and a "giant" connected component with small average distances between vertices.^[9]

Collaboration distance

The distance between two people/nodes in a collaboration graph is called the collaboration distance.^[10] Thus the collaboration distance between two distinct nodes is equal to the smallest number of edges in an edge-path connecting them. If no path connecting two nodes in a collaboration graph exists, the collaboration distance between them is said to be infinite.

The collaboration distance may be used, for instance, for evaluating the citations of an author, a group of authors or a journal ^[11].

In the collaboration graph of mathematicians, the collaboration distance from a particular person to Paul Erdős is called the Erdős number of that person. MathSciNet has a free online tool^[12] for computing the collaboration distance between any two mathematicians as well as the Erdős number of a mathematician. This tool also shows the actual chain of co-authors that realizes the collaboration distance.

For the Hollywood graph, an analog of the Erdős number, called the Bacon number, has also been considered, which measures the collaboration distance to Kevin Bacon.

Generalizations of the collaboration graph

Some generalizations of the collaboration graph of mathematicians have also been considered. There is a hypergraph version,^[13] where individual mathematicians are vertices and where a group of mathematicians (not necessarily just two) constitutes a hyperedge if there is a paper that where they all were co-authors. Another variation is a simple graph where two mathematicians are joined by an edge if and only if there is a paper with only two of them (and no others) as co-authors.

A multigraph version of a collaboration graph has also been considered where two mathematicians are joined by k edges if they co-authored exactly k papers together. Another variation is a weighted collaboration graph where with rational weights where two mathematicians are joined by an edge with weight $\tfrac{1}{k}$ whenever they co-authored exactly k papers together.^[14] This model naturally leads to the notion of a "rational Erdős number".^[15]

See also

• Erdős number
• Bacon number
• Graph theory

References

1. ^ Odda, Tom (1979). "On properties of a well-known graph or what is your Ramsey number? Topics in graph theory." (in English). Annals of the New York Academy of Sciences (New York, 1977: New York Academy of Sciences) 328: 166–172. doi:10.1111/j.1749-6632.1979.tb17777.x. 
2. ^ Frank Harary. Topics in Graph Theory. New York Academy of Sciences, 1979. ISBN 0897660285
3. ^ Vladimir Batagelj and Andrej Mrvar, Some analyses of Erdős collaboration graph. Social Networks, vol. 22 (2000), no. 2, pp. 173–186.
4. ^ Casper Goffman. And what is your Erdős number?, American Mathematical Monthly, vol. 76 (1979), p. 791
5. ^ Chaomei Chen, C. Chen. Mapping Scientific Frontiers: The Quest for Knowledge Visualization. Springer-Verlag New York. January 2003. ISBN 9781852334949. See p. 94.
6. ^ Fan Chung, Linyuan Lu. Complex Graphs and Networks, Vol. 107. American Mathematical Society. October 2006. ISBN 9780821836576. See p. 16
7. ^ Albert-László Barabási and Réka Albert, Emergence of scaling in random networks. Science, vol. 286 (1999), no. 5439, pp. 509–512
8. ^ V. Boginski, S. Butenko, P.M. Pardalos, O. Prokopyev. Collaboration networks in sports. pp. 265–277. Economics, Management, and Optimization in Sports. Springer-Verlag, New York, February 2004. ISBN 9783540207122
9. ^ Jerrold W. Grossman. The evolution of the mathematical research collaboration graph. Proceedings of the Thirty-third Southeastern International Conference on Combinatorics, Graph Theory and Computing (Boca Raton, FL, 2002). Congressus Numerantium. Vol. 158 (2002), pp. 201–212.
10. ^ Deza, Elena; Deza, Michel-Marie (2006). "Ch. 22". Dictionary of Distances. Elsevier. p. 279. ISBN 9780444520876. .
11. ^ Bras-Amorós, M.; Domingo-Ferrer, J.; Torra, V; (2011). "A bibliometric index based on the collaboration distance between cited and citing authors". Journal of Informetrics 5 (2): 248-264. doi:10.1016/j.joi.2010.11.001. http://www.sciencedirect.com/science/article/pii/S1751157710000970.  edit
12. ^ MathSciNet Collaboration Distance Calculator. American Mathematical Society. Accessed May 23, 2008
13. ^ Frank Harary. Topics in Graph Theory. New York Academy of Sciences, 1979. ISBN 0897660285 See p. 166
14. ^ Mark E.J. Newman. Who Is the Best Connected Scientist? A Study of Scientific Coauthorship Networks. Lecture Notes in Physics, vol. 650, pp. 337–370. Springer-Verlag. Berlin. 2004. ISBN 978-3-540-22354-2.
15. ^ Alexandru T. Balaban and Douglas J. Klein.Co-authorship, rational Erdős numbers, and resistance distances in graphs. Scientometrics, vol. 55 (2002), no. 1, pp. 59–70.

External links

• Collaboration graph of the University of Georgia Mathematics Department
• Collaboration graph of the University of Oakland Mathematics and Statistics Department