Link prediction for interdisciplinary collaboration via co-authorship
  network

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Abstract

We analyse the Publication and Research (PURE) data set of University of Bristol collected between \(2008\) and \(2013\). Using the existing co-authorship network and academic information thereof, we propose a new link prediction methodology, with the specific aim of identifying potential interdisciplinary collaboration in a university-wide collaboration network.

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The link-prediction problem for social networks

David Liben-Nowell, Jon Kleinberg (2007)

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Is Open Access

Resolution limit in community detection

Santo Fortunato, Marc Barthelemy (2006)

Detecting community structure is fundamental to clarify the link between structure and function in complex networks and is used for practical applications in many disciplines. A successful method relies on the optimization of a quantity called modularity [Newman and Girvan, Phys. Rev. E 69, 026113 (2004)], which is a quality index of a partition of a network into communities. We find that modularity optimization may fail to identify modules smaller than a scale which depends on the total number L of links of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined. The probability that a module conceals well-defined substructures is the highest if the number of links internal to the module is of the order of \sqrt{2L} or smaller. We discuss the practical consequences of this result by analyzing partitions obtained through modularity optimization in artificial and real networks.