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      Citation-based clustering of publications using CitNetExplorer and VOSviewer

      research-article
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      Scientometrics
      Springer Netherlands
      Citation, Clustering, CitNetExplorer, VOSviewer

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          Abstract

          Clustering scientific publications in an important problem in bibliometric research. We demonstrate how two software tools, CitNetExplorer and VOSviewer, can be used to cluster publications and to analyze the resulting clustering solutions. CitNetExplorer is used to cluster a large set of publications in the field of astronomy and astrophysics. The publications are clustered based on direct citation relations. CitNetExplorer and VOSviewer are used together to analyze the resulting clustering solutions. Both tools use visualizations to support the analysis of the clustering solutions, with CitNetExplorer focusing on the analysis at the level of individual publications and VOSviewer focusing on the analysis at an aggregate level. The demonstration provided in this paper shows how a clustering of publications can be created and analyzed using freely available software tools. Using the approach presented in this paper, bibliometricians are able to carry out sophisticated cluster analyses without the need to have a deep knowledge of clustering techniques and without requiring advanced computer skills.

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          Most cited references16

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          Finding and evaluating community structure in networks

          We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
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            • Record: found
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            Is Open Access

            Fast unfolding of communities in large networks

            We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks. .
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              • Record: found
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              Maps of random walks on complex networks reveal community structure

              To comprehend the multipartite organization of large-scale biological and social systems, we introduce a new information theoretic approach that reveals community structure in weighted and directed networks. The method decomposes a network into modules by optimally compressing a description of information flows on the network. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of more than 6000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network -- including physics, chemistry, molecular biology, and medicine -- information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.
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                Author and article information

                Contributors
                ecknjpvan@cwts.leidenuniv.nl
                waltmanlr@cwts.leidenuniv.nl
                Journal
                Scientometrics
                Scientometrics
                Scientometrics
                Springer Netherlands (Dordrecht )
                0138-9130
                27 February 2017
                27 February 2017
                2017
                : 111
                : 2
                : 1053-1070
                Affiliations
                ISNI 0000 0001 2312 1970, GRID grid.5132.5, Centre for Science and Technology Studies, , Leiden University, ; Leiden, The Netherlands
                Article
                2300
                10.1007/s11192-017-2300-7
                5400793
                28490825
                cfe110ff-96ae-490a-8872-ee1557442831
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 6 June 2016
                Categories
                Article
                Custom metadata
                © Akadémiai Kiadó, Budapest, Hungary 2017

                Computer science
                citation,clustering,citnetexplorer,vosviewer
                Computer science
                citation, clustering, citnetexplorer, vosviewer

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