Community Structure and the Evolution of Interdisciplinarity in Slovenia's Scientific Collaboration Network

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Abstract

Interaction among the scientific disciplines is of vital importance in modern science. Focusing on the case of Slovenia, we study the dynamics of interdisciplinary sciences from to . Our approach relies on quantifying the interdisciplinarity of research communities detected in the coauthorship network of Slovenian scientists over time. Examining the evolution of the community structure, we find that the frequency of interdisciplinary research is only proportional with the overall growth of the network. Although marginal improvements in favor of interdisciplinarity are inferable during the 70s and 80s, the overall trends during the past 20 years are constant and indicative of stalemate. We conclude that the flow of knowledge between different fields of research in Slovenia is in need of further stimulation.

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Modularity and community structure in networks

M. Newman (2006)

Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.

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Uncovering the overlapping community structure of complex networks in nature and society

Illés J Farkas, Gergely Palla, Tamás Vicsek … (2005)

Many complex systems in nature and society can be described in terms of networks capturing the intricate web of connections among the units they are made of. A key question is how to interpret the global organization of such networks as the coexistence of their structural subunits (communities) associated with more highly interconnected parts. Identifying these a priori unknown building blocks (such as functionally related proteins, industrial sectors and groups of people) is crucial to the understanding of the structural and functional properties of networks. The existing deterministic methods used for large networks find separated communities, whereas most of the actual networks are made of highly overlapping cohesive groups of nodes. Here we introduce an approach to analysing the main statistical features of the interwoven sets of overlapping communities that makes a step towards uncovering the modular structure of complex systems. After defining a set of new characteristic quantities for the statistics of communities, we apply an efficient technique for exploring overlapping communities on a large scale. We find that overlaps are significant, and the distributions we introduce reveal universal features of networks. Our studies of collaboration, word-association and protein interaction graphs show that the web of communities has non-trivial correlations and specific scaling properties.

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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.

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Author and article information

Contributors

Matthias Dehmer: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, USA )

ISSN (Electronic): 1932-6203

Publication date Collection: 2014

Publication date (Electronic): 11 April 2014

Volume: 9

Issue: 4

Electronic Location Identifier: e94429

Affiliations

[1 ]Faculty of Information Studies in Novo mesto, Novo mesto, Slovenia

[2 ]Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia

UMIT, Austria

Author notes

* E-mail: matjaz.perc@ 123456uni-mb.si

Competing Interests: Matjaž Perc is a PLOS ONE Editorial Board member. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

Conceived and designed the experiments: BL ZL JP MP. Performed the experiments: BL. Analyzed the data: BL ZL JP MP. Contributed reagents/materials/analysis tools: BL ZL JP MP. Wrote the paper: BL ZL MP.

Article

Publisher ID: PONE-D-14-03956

DOI: 10.1371/journal.pone.0094429

PMC ID: 3984150

PubMed ID: 24728345

SO-VID: 68af4696-41ea-4042-836a-56e411e76b8f

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This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 26 January 2014

Date accepted : 16 March 2014

Page count

Pages: 5

Funding

This research was supported by the Slovenian Research Agency ARRS (Grants J1-4055, J1-5454, L7-4119 and Program P1-0383), as well as by The European Regional Development Fund (Creative Core Grant FISNM-3330-13-500033). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Community Structure and the Evolution of Interdisciplinarity in Slovenia's Scientific Collaboration Network

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Most cited references 12

Modularity and community structure in networks

Uncovering the overlapping community structure of complex networks in nature and society

Resolution limit in community detection

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