Science Mapping Analysis with bibliometrix R-package: an example

Massimo Aria

May 25, 2018

The collection

The collection is composed by all articles, letters, review and proceedings papers published on the Journal of Informetrics until 2017 (data collected in May, 2018).

Data source: Clarivate Analytics Web of Science (http://apps.webofknowledge.com)

Data format: Plaintext

Timespan: 2007-2017

Install and load bibliometrix R-package

# Stable version from CRAN (Comprehensive R Archive Network)
# if you need to execute the code, remove # from the beginning of the next line

# install.packages("bibliometrix")


# Most updated version from GitHub
# if you need to execute the code, remove # from the beginning of the next lines

# install.packages("devtools")
# devtools::install_github("massimoaria/bibliometrix")

library(bibliometrix)
## To cite bibliometrix in publications, please use:
## 
## Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), pp 959-975, Elsevier.
## 
## http:\\www.bibliometrix.org

Data Loading and Converting

# Loading txt or bib files into R environment
D <- readFiles("http://bibliometrix.org/datasets/joi.txt")

# Converting the loaded files into a R bibliographic dataframe
M <- convert2df(D, dbsource="isi",format="plaintext")
## 
## Converting your isi collection into a bibliographic dataframe
## 
## Articles extracted   100 
## Articles extracted   200 
## Articles extracted   300 
## Articles extracted   400 
## Articles extracted   500 
## Articles extracted   600 
## Articles extracted   700 
## Articles extracted   770 
## Done!
## 
## 
## Generating affiliation field tag AU_UN from C1:  Done!

Descriptive Analysis

Main findings about the collection

#options(width=160)
results <- biblioAnalysis(M)
summary(results, k=10, pause=F, width=130)


Main Information about data

 Documents                             770 
 Sources (Journals, Books, etc.)       1 
 Keywords Plus (ID)                    1104 
 Author's Keywords (DE)                1991 
 Period                                2007 - 2017 
 Average citations per documents       16.53 

 Authors                               987 
 Author Appearances                    1882 
 Authors of single authored documents  69 
 Authors of multi authored documents   918 

 Documents per Author                  0.78 
 Authors per Document                  1.28 
 Co-Authors per Documents              2.44 
 Collaboration Index                   1.62 
 
 Document types                     
 J                                     770 
 

Annual Scientific Production

 Year    Articles
    2007       32
    2008       34
    2009       35
    2010       68
    2011       67
    2012       75
    2013      102
    2014       89
    2015       82
    2016       83
    2017      103

Annual Percentage Growth Rate 12.40062 


Most Productive Authors

   Authors        Articles Authors        Articles Fractionalized
1   BORNMANN L          55  BORNMANN L                      26.25
2   LEYDESDORFF L       35  LEYDESDORFF L                   17.00
3   ROUSSEAU R          33  THELWALL M                      16.70
4   ABRAMO G            31  ROUSSEAU R                      14.50
5   D'ANGELO CA         29  EGGHE L                         13.17
6   THELWALL M          27  ABRAMO G                        12.00
7   WALTMAN L           21  SCHREIBER M                     11.33
8   DANIEL HD           15  D'ANGELO CA                     11.33
9   DING Y              15  KOSMULSKI M                     11.00
10  EGGHE L             15  WALTMAN L                        9.82


Top manuscripts per citations

                                                                                    Paper           TC TCperYear
1  ALONSO S;CABRERIZO FJ;HERRERA-VIEDMA E;HERRERA F,(2009),J. INFORMETR.                           254      28.2
2  MOED HF,(2010),J. INFORMETR.                                                                    251      31.4
3  COSTAS R;BORDONS M,(2007),J. INFORMETR.                                                         189      17.2
4  WAGNER CS;ROESSNER JD;BOBB K;KLEIN JT;BOYACK KW;KEYTON J;RAFOLS I;BORNER K,(2011),J. INFORMETR. 186      26.6
5  GONZALEZ-PEREIRA B;GUERRERO-BOTE VP;MOYA-ANEGON F,(2010),J. INFORMETR.                          181      22.6
6  PRABOWO R;THELWALL M,(2009),J. INFORMETR.                                                       180      20.0
7  CHEN P;XIE H;MASLOV S;REDNER S,(2007),J. INFORMETR.                                             177      16.1
8  WALTMAN L;VAN ECK NJ;VAN LEEUWEN TN;VISSER MS;VAN RAAN AFJ,(2011),J. INFORMETR.                 176      25.1
9  CRAIG ID;PLUME AM;MCVEIGH ME;PRINGLE J;AMIN M,(2007),J. INFORMETR.                              152      13.8
10 WALTMAN L;VAN ECK NJ;NOYONS ECM,(2010),J. INFORMETR.                                            149      18.6


Most Productive Countries (of corresponding authors)

     Country   Articles   Freq SCP MCP
1  USA               80 0.1042  57  23
2  CHINA             75 0.0977  41  34
3  ITALY             68 0.0885  62   6
4  GERMANY           62 0.0807  35  27
5  SPAIN             58 0.0755  45  13
6  NETHERLANDS       57 0.0742  40  17
7  BELGIUM           51 0.0664  26  25
8  ENGLAND           44 0.0573  35   9
9  POLAND            25 0.0326  23   2
10 SWITZERLAND       22 0.0286  12  10


SCP: Single Country Publications

MCP: Multiple Country Publications


Total Citations per Country

     Country      Total Citations Average Article Citations
1  NETHERLANDS               2052                     36.00
2  USA                       1554                     19.43
3  SPAIN                     1418                     24.45
4  ENGLAND                   1120                     25.45
5  GERMANY                   1076                     17.35
6  ITALY                      746                     10.97
7  CHINA                      691                      9.21
8  BELGIUM                    558                     10.94
9  SWITZERLAND                435                     19.77
10 CANADA                     346                     16.48


Most Relevant Sources

           Sources        Articles
1 JOURNAL OF INFORMETRICS      770


Most Relevant Keywords

   Author Keywords (DE)      Articles Keywords-Plus (ID)     Articles
1        BIBLIOMETRICS             80   SCIENCE                   207
2        CITATION ANALYSIS         78   IMPACT                    127
3        H-INDEX                   69   INDICATORS                 98
4        RESEARCH EVALUATION       44   H-INDEX                    73
5        CITATIONS                 27   JOURNALS                   63
6        G-INDEX                   26   INDEX                      50
7        IMPACT FACTOR             24   PUBLICATION                50
8        SCIENTOMETRICS            23   PERFORMANCE                46
9        HIRSCH INDEX              19   NETWORKS                   45
10       COLLABORATION             18   RESEARCH PERFORMANCE       43
plot(x=results, k=10, pause=F)

Most Cited References

CR <- citations(M, field = "article", sep = ";")
cbind(CR$Cited[1:20])
                                                                                                           [,1]
HIRSCH JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 101073/PNAS0507655102                               217
EGGHE L, 2006, SCIENTOMETRICS, V69, P131, DOI 101007/S11192-006-0144-7                                       96
RADICCHI F, 2008, P NATL ACAD SCI USA, V105, P17268, DOI 101073/PNAS0806977105                               58
WALTMAN L, 2011, J INFORMETR, V5, P37, DOI 101016/JJOI201008001                                              54
JIN BH, 2007, CHINESE SCI BULL, V52, P855, DOI 101007/S11434-007-0145-9                                      48
MOED HF, 2010, J INFORMETR, V4, P265, DOI 101016/JJOI201001002                                               48
GARFIELD E, 1972, SCIENCE, V178, P471, DOI 101126/SCIENCE1784060471                                          46
LUNDBERG J, 2007, J INFORMETR, V1, P145, DOI 101016/JJOI200609007                                            45
BORNMANN L, 2008, J DOC, V64, P45, DOI 101108/00220410810844150                                              43
EGGHE L, 2005, LIBR INFORM SCI SER, P1                                                                       40
SEGLEN PO, 1992, J AM SOC INFORM SCI, V43, P628, DOI 101002/(SICI)1097-4571(199210)43:9<628::AID-ASI5>30CO   40
VAN RAAN AFJ, 2006, SCIENTOMETRICS, V67, P491, DOI 101556/SCIENT672006310                                    39
ALONSO S, 2009, J INFORMETR, V3, P273, DOI 101016/JJOI200904001                                              37
MERTON RK, 1968, SCIENCE, V159, P56, DOI 101126/SCIENCE159381056                                             37
MOED H F, 2005, CITATION ANAL RES EV                                                                         37
OPTHOF T, 2010, J INFORMETR, V4, P423, DOI 101016/JJOI201002003                                              37
PINSKI G, 1976, INFORM PROCESS MANAG, V12, P297, DOI 101016/0306-4573(76)90048-0                             37
EGGHE L, 2006, SCIENTOMETRICS, V69, P121, DOI 101007/S11192-006-0143-8                                       36
PRICE DJD, 1965, SCIENCE, V149, P510                                                                         35
GARFIELD E, 2006, JAMA-J AM MED ASSOC, V295, P90, DOI 101001/JAMA295190                                      34

Network Analysis of main relational dimensions

Co-citation Analysis: the Intellectual Structure of the field

Plot options:

  • n = 50 (the funxtion plots the main 50 cited references)

  • type = “fruchterman” (the network layout is generated using the Fruchterman-Reingold Algorithm)

  • size.cex = TRUE (the size of the vertices is proportional to their degree)

  • size = 20 (the max size of vertices)

  • remove.multiple=FALSE (multiple edges are not removed)

  • labelsize = 0.7 (defines the size of vertex labels)

  • edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness)

  • edges.min = 5 (plots only edges with a strength greater than or equal to 5)

  • all other arguments assume the default values

NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=FALSE, labelsize=0.7,edgesize = 10, edges.min=5)

Descriptive analysis of co-citation network characteristics

netstat <- networkStat(net$graph)
summary(netstat,k=10)


Main statistics about the network

 Size                                  50 
 Density                               0.849 
 Transitivity                          0.88 
 Diameter                              2 
 Degree Centralization                 0.151 
 Closeness Centralization              0.254 
 Betweenness Centralization            0.003 
 Eigenvector Centralization            0.139 
 Average path length                   1.151 
 





Main measures of centrality and prestige of vertices


Degree Centrality: Top vertices

   Vertex ID              Degree Centrality
1       HIRSCH JE 2005-1              1.000
2       EGGHE L 2006-2                1.000
3       BATISTA PD 2006               0.980
4       BOLLEN J 2006-1               0.959
5       VAN RAAN 2004-1               0.959
6       VAN RAAN 2006-2               0.959
7       PINSKI G 1976                 0.959
8       PERSSON O 2004-1              0.959
9       MOED H. 2005-1                0.939
10      BORNMANN L 2008-1             0.939


Closeness Centrality: Top vertices

   Vertex ID              Closeness Centrality
1       HIRSCH JE 2005-1                 1.000
2       EGGHE L 2006-2                   1.000
3       BATISTA PD 2006                  0.980
4       BOLLEN J 2006-1                  0.961
5       VAN RAAN 2004-1                  0.961
6       VAN RAAN 2006-2                  0.961
7       PINSKI G 1976                    0.961
8       PERSSON O 2004-1                 0.961
9       MOED H. 2005-1                   0.942
10      BORNMANN L 2008-1                0.942


Eigenvector Centrality: Top vertices

   Vertex ID              Eigenvector Centrality
1        EGGHE L 2006-2                    1.000
2        HIRSCH JE 2005-1                  1.000
3        BATISTA PD 2006                   0.984
4        PINSKI G 1976                     0.975
5        BOLLEN J 2006-1                   0.972
6        VAN RAAN 2004-1                   0.972
7        PERSSON O 2004-1                  0.969
8        VAN RAAN 2006-2                   0.967
9        MOED H. 2005-1                    0.957
10       CRONIN B 2001-1                   0.956


Betweenness Centrality: Top vertices

   Vertex ID              Betweenness Centrality
1       HIRSCH JE 2005-1                 0.00582
2       EGGHE L 2006-2                   0.00582
3       BATISTA PD 2006                  0.00542
4       WUCHTY S 2007                    0.00491
5       BORNMANN L 2008-1                0.00479
6       PERSSON O 2004-1                 0.00478
7       VAN RAAN 2006-2                  0.00475
8       GLANZEL W 2002-1                 0.00450
9       EGGHE L 2005-1                   0.00447
10      BOLLEN J 2006-1                  0.00443


PageRank Score: Top vertices

   Vertex ID              Pagerank Score
1       HIRSCH JE 2005-1          0.0231
2       EGGHE L 2006-2            0.0231
3       BATISTA PD 2006           0.0227
4       PERSSON O 2004-1          0.0222
5       VAN RAAN 2006-2           0.0222
6       VAN RAAN 2004-1           0.0222
7       BOLLEN J 2006-1           0.0222
8       PINSKI G 1976             0.0222
9       BORNMANN L 2008-1         0.0218
10      GLANZEL W 2002-1          0.0218


Hub Score: Top vertices

   Vertex ID              Hub Score
1        HIRSCH JE 2005-1     1.000
2        EGGHE L 2006-2       1.000
3        BATISTA PD 2006      0.984
4        PINSKI G 1976        0.975
5        BOLLEN J 2006-1      0.972
6        VAN RAAN 2004-1      0.972
7        PERSSON O 2004-1     0.969
8        VAN RAAN 2006-2      0.967
9        MOED H. 2005-1       0.957
10       CRONIN B 2001-1      0.956


Authority Score: Top vertices

   Vertex ID              Authority Score
1        HIRSCH JE 2005-1           1.000
2        EGGHE L 2006-2             1.000
3        BATISTA PD 2006            0.984
4        PINSKI G 1976              0.975
5        BOLLEN J 2006-1            0.972
6        VAN RAAN 2004-1            0.972
7        PERSSON O 2004-1           0.969
8        VAN RAAN 2006-2            0.967
9        MOED H. 2005-1             0.957
10       CRONIN B 2001-1            0.956


Overall Ranking: Top vertices

   Vertex ID              Overall Ranking
1       HIRSCH JE 2005-1              1.0
2       EGGHE L 2006-2                2.0
3       BATISTA PD 2006               3.0
4       PERSSON O 2004-1              4.0
5       VAN RAAN 2006-2               5.0
6       BOLLEN J 2006-1               6.5
7       VAN RAAN 2004-1               6.5
8       PINSKI G 1976                 8.0
9       BORNMANN L 2008-1             9.0
10      GLANZEL W 2002-1             10.0

Keyword co-occurrences network

Plot options:

  • normalize = “association” (the vertex similarities are normalized using association strength)

  • n = 50 (the function plots the main 50 cited references)

  • type = “fruchterman” (the network layout is generated using the Fruchterman-Reingold Algorithm)

  • size.cex = TRUE (the size of the vertices is proportional to their degree)

  • size = 20 (the max size of the vertices)

  • remove.multiple=FALSE (multiple edges are not removed)

  • labelsize = 3 (defines the max size of vertex labels)

  • label.cex = TRUE (The vertex label sizes are proportional to their degree)

  • edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness)

  • label.n = 30 (Labels are plotted only for the main 30 vertices)

  • edges.min = 25 (plots only edges with a strength greater than or equal to 2)

  • all other arguments assume the default values

NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
net=networkPlot(NetMatrix, normalize="association", n = 50, Title = "Keyword Co-occurrences", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=F, edgesize = 10, labelsize=3,label.cex=TRUE,label.n=30,edges.min=2)

Descriptive analysis of keyword co-occurrences network characteristics

netstat <- networkStat(NetMatrix)
summary(netstat,k=10)


Main statistics about the network

 Size                                  1104 
 Density                               0.017 
 Transitivity                          0.193 
 Diameter                              5 
 Degree Centralization                 0.435 
 Closeness Centralization              0.005 
 Betweenness Centralization            0.226 
 Eigenvector Centralization            0.931 
 Average path length                   2.549 
 





Main measures of centrality and prestige of vertices


Degree Centrality: Top vertices

   Vertex ID              Degree Centrality
1    SCIENCE                          0.451
2    IMPACT                           0.311
3    INDICATORS                       0.268
4    JOURNALS                         0.208
5    PERFORMANCE                      0.180
6    H-INDEX                          0.180
7    NETWORKS                         0.173
8    PUBLICATION                      0.170
9    RESEARCH PERFORMANCE             0.149
10   PATTERNS                         0.147


Closeness Centrality: Top vertices

   Vertex ID              Closeness Centrality
1    SCIENCE                            0.0408
2    IMPACT                             0.0405
3    INDICATORS                         0.0404
4    JOURNALS                           0.0403
5    NETWORKS                           0.0402
6    H-INDEX                            0.0402
7    PERFORMANCE                        0.0402
8    PUBLICATION                        0.0402
9    PATTERNS                           0.0402
10   RESEARCH PERFORMANCE               0.0402


Eigenvector Centrality: Top vertices

   Vertex ID              Eigenvector Centrality
1    SCIENCE                               1.000
2    IMPACT                                0.798
3    INDICATORS                            0.728
4    JOURNALS                              0.625
5    PERFORMANCE                           0.566
6    H-INDEX                               0.565
7    PUBLICATION                           0.562
8    NETWORKS                              0.529
9    RESEARCH PERFORMANCE                  0.515
10   PATTERNS                              0.511


Betweenness Centrality: Top vertices

   Vertex ID              Betweenness Centrality
1            SCIENCE                      0.2270
2            IMPACT                       0.1123
3            INDICATORS                   0.0756
4            JOURNALS                     0.0608
5            NETWORKS                     0.0437
6            H-INDEX                      0.0414
7            PERFORMANCE                  0.0343
8            PUBLICATION                  0.0240
9            PATTERNS                     0.0217
10           PUBLICATIONS                 0.0215


PageRank Score: Top vertices

   Vertex ID              Pagerank Score
1    SCIENCE                     0.02164
2    IMPACT                      0.01472
3    INDICATORS                  0.01230
4    JOURNALS                    0.00981
5    H-INDEX                     0.00847
6    NETWORKS                    0.00816
7    PERFORMANCE                 0.00794
8    PUBLICATION                 0.00754
9    RESEARCH PERFORMANCE        0.00652
10   PATTERNS                    0.00647


Hub Score: Top vertices

   Vertex ID              Hub Score
1    SCIENCE                  1.000
2    IMPACT                   0.798
3    INDICATORS               0.728
4    JOURNALS                 0.625
5    PERFORMANCE              0.566
6    H-INDEX                  0.565
7    PUBLICATION              0.562
8    NETWORKS                 0.529
9    RESEARCH PERFORMANCE     0.515
10   PATTERNS                 0.511


Authority Score: Top vertices

   Vertex ID              Authority Score
1    SCIENCE                        1.000
2    IMPACT                         0.798
3    INDICATORS                     0.728
4    JOURNALS                       0.625
5    PERFORMANCE                    0.566
6    H-INDEX                        0.565
7    PUBLICATION                    0.562
8    NETWORKS                       0.529
9    RESEARCH PERFORMANCE           0.515
10   PATTERNS                       0.511


Overall Ranking: Top vertices

   Vertex ID              Overall Ranking
1    SCIENCE                            1
2    IMPACT                             2
3    INDICATORS                         3
4    JOURNALS                           4
5    H-INDEX                            5
6    PERFORMANCE                        6
7    NETWORKS                           7
8    PUBLICATION                        8
9    RESEARCH PERFORMANCE               9
10   PATTERNS                          10

Edu collaboration network

NetMatrix <- biblioNetwork(M, analysis = "collaboration",  network = "universities", sep = ";")
net=networkPlot(NetMatrix,  n = 50, Title = "Edu collaboration",type = "auto", size=10,size.cex=T,edgesize = 3,labelsize=0.6)

Descriptive analysis of edu collaboration network characteristics

netstat <- networkStat(NetMatrix)
summary(netstat,k=15)


Main statistics about the network

 Size                                  533 
 Density                               0.005 
 Transitivity                          0.297 
 Diameter                              12 
 Degree Centralization                 0.069 
 Closeness Centralization              0.002 
 Betweenness Centralization            0.066 
 Eigenvector Centralization            0.967 
 Average path length                   4.565 
 





Main measures of centrality and prestige of vertices


Degree Centrality: Top vertices

        Vertex ID              Degree Centrality
1  KATHOLIEKE UNIV LEUVEN                 0.0733
2  UNIV ANTWERP                           0.0545
3  INDIANA UNIV                           0.0526
4  WUHAN UNIV                             0.0432
5  LEIDEN UNIV                            0.0395
6  UNIV AMSTERDAM                         0.0338
7  CHINESE ACAD SCI                       0.0338
8  TONGJI UNIV                            0.0320
9  NANJING UNIV                           0.0301
10 UNIV SUSSEX                            0.0282
11 DIV SCI AND INNOVAT STUDIES            0.0263
12 ZHEJIANG UNIV                          0.0263
13 VRIJE UNIV AMSTERDAM                   0.0244
14 NATL TAIWAN UNIV                       0.0226
15 UNIV GRANADA                           0.0207


Closeness Centrality: Top vertices

        Vertex ID              Closeness Centrality
1  INDIANA UNIV                             0.00338
2  UNIV AMSTERDAM                           0.00338
3  UNIV ANTWERP                             0.00338
4  KATHOLIEKE UNIV LEUVEN                   0.00338
5  UNIV SUSSEX                              0.00338
6  NANJING UNIV                             0.00338
7  ZHEJIANG UNIV                            0.00338
8  DIV SCI AND INNOVAT STUDIES              0.00338
9  WUHAN UNIV                               0.00338
10 TONGJI UNIV                              0.00338
11 CHINESE ACAD SCI                         0.00338
12 LEIDEN UNIV                              0.00338
13 UNIV CARLOS III MADRID                   0.00338
14 UNIV MONTREAL                            0.00338
15 NATL TAIWAN UNIV                         0.00338


Eigenvector Centrality: Top vertices

   Vertex ID              Eigenvector Centrality
1  KATHOLIEKE UNIV LEUVEN                  1.000
2  UNIV ANTWERP                            0.833
3  TONGJI UNIV                             0.573
4  INDIANA UNIV                            0.551
5  ZHEJIANG UNIV                           0.544
6  CHINESE ACAD SCI                        0.539
7  KHBO ASSOC KU LEUVEN                    0.472
8  NANJING UNIV                            0.456
9  WUHAN UNIV                              0.412
10 UNIV AMSTERDAM                          0.410
11 HENAN NORMAL UNIV                       0.344
12 HASSELT UNIV                            0.332
13 TSINGHUA UNIV                           0.331
14 YONSEI UNIV                             0.306
15 LIB ZHEJIANG UNIV                       0.306


Betweenness Centrality: Top vertices

   Vertex ID              Betweenness Centrality
1  INDIANA UNIV                           0.0668
2  UNIV ROMA LA SAPIENZA                  0.0387
3  LEIDEN UNIV                            0.0362
4  KATHOLIEKE UNIV LEUVEN                 0.0318
5  UNIV AMSTERDAM                         0.0306
6  CNR                                    0.0295
7  UNIV ANTWERP                           0.0290
8  WUHAN UNIV                             0.0262
9  NATL TAIWAN UNIV                       0.0260
10 UNIV CARLOS III MADRID                 0.0249
11 BAR ILAN UNIV                          0.0233
12 UNIV SUSSEX                            0.0191
13 UNIV ROMA TOR VERGATA                  0.0174
14 UNIV SIENA                             0.0162
15 NANJING UNIV                           0.0156


PageRank Score: Top vertices

        Vertex ID              Pagerank Score
1  KATHOLIEKE UNIV LEUVEN             0.01463
2  LEIDEN UNIV                        0.01162
3  INDIANA UNIV                       0.01127
4  UNIV ANTWERP                       0.01086
5  WUHAN UNIV                         0.01051
6  UNIV AMSTERDAM                     0.00830
7  NANJING UNIV                       0.00682
8  CHINESE ACAD SCI                   0.00679
9  VRIJE UNIV AMSTERDAM               0.00664
10 UNIV SUSSEX                        0.00651
11 DIV SCI AND INNOVAT STUDIES        0.00633
12 NATL TAIWAN UNIV                   0.00631
13 TONGJI UNIV                        0.00611
14 UNIV GRANADA                       0.00602
15 UNIV VALENCIA                      0.00536


Hub Score: Top vertices

   Vertex ID              Hub Score
1  KATHOLIEKE UNIV LEUVEN     1.000
2  UNIV ANTWERP               0.833
3  TONGJI UNIV                0.573
4  INDIANA UNIV               0.551
5  ZHEJIANG UNIV              0.544
6  CHINESE ACAD SCI           0.539
7  KHBO ASSOC KU LEUVEN       0.472
8  NANJING UNIV               0.456
9  WUHAN UNIV                 0.412
10 UNIV AMSTERDAM             0.410
11 HENAN NORMAL UNIV          0.344
12 HASSELT UNIV               0.332
13 TSINGHUA UNIV              0.331
14 YONSEI UNIV                0.306
15 LIB ZHEJIANG UNIV          0.306


Authority Score: Top vertices

   Vertex ID              Authority Score
1  KATHOLIEKE UNIV LEUVEN           1.000
2  UNIV ANTWERP                     0.833
3  TONGJI UNIV                      0.573
4  INDIANA UNIV                     0.551
5  ZHEJIANG UNIV                    0.544
6  CHINESE ACAD SCI                 0.539
7  KHBO ASSOC KU LEUVEN             0.472
8  NANJING UNIV                     0.456
9  WUHAN UNIV                       0.412
10 UNIV AMSTERDAM                   0.410
11 HENAN NORMAL UNIV                0.344
12 HASSELT UNIV                     0.332
13 TSINGHUA UNIV                    0.331
14 YONSEI UNIV                      0.306
15 LIB ZHEJIANG UNIV                0.306


Overall Ranking: Top vertices

        Vertex ID              Overall Ranking
1  KATHOLIEKE UNIV LEUVEN                    1
2  UNIV ANTWERP                              2
3  INDIANA UNIV                              3
4  WUHAN UNIV                                4
5  CHINESE ACAD SCI                          5
6  UNIV AMSTERDAM                            6
7  TONGJI UNIV                               7
8  ZHEJIANG UNIV                             8
9  NANJING UNIV                              9
10 LEIDEN UNIV                              10
11 KHBO ASSOC KU LEUVEN                     11
12 DIV SCI AND INNOVAT STUDIES              12
13 UNIV SUSSEX                              13
14 NATL TAIWAN UNIV                         14
15 UNIV CARLOS III MADRID                   15

Co-Word Analysis: The conceptual structure of the field

CS <- conceptualStructure(M, method="CA", field="ID", minDegree=10, k.max = 8, stemming=f, labelsize=8,documents=20)

Historiograph

histResults <- histNetwork(M, sep = ";")
## Articles analysed   100 
## Articles analysed   200 
## Articles analysed   300 
## Articles analysed   400 
## Articles analysed   500 
## Articles analysed   600 
## Articles analysed   700 
## Articles analysed   770
options(width = 130)
net <- histPlot(histResults, n=20, size.cex=TRUE, size = 5, arrowsize = 0.5)


 Legend

                                           Paper                       DOI Year LCS GCS
2007 - 3             COSTAS R, 2007, J INFORMETR 10.1016/J.JOI.2007.02.001 2007  21 189
2007 - 13          LUNDBERG J, 2007, J INFORMETR 10.1016/J.JOI.2006.09.007 2007  45 136
2008 - 33          VAN ECK NJ, 2008, J INFORMETR 10.1016/J.JOI.2008.09.004 2008  22  66
2009 - 67            ALONSO S, 2009, J INFORMETR 10.1016/J.JOI.2009.04.001 2009  37 254
2010 - 110      FRANCESCHET M, 2010, J INFORMETR 10.1016/J.JOI.2010.06.003 2010  21  66
2010 - 113          VIEIRA ES, 2010, J INFORMETR 10.1016/J.JOI.2010.06.006 2010  22  12
2010 - 122         BORNMANN L, 2010, J INFORMETR 10.1016/J.JOI.2009.10.004 2010  28  44
2010 - 128            MOED HF, 2010, J INFORMETR 10.1016/J.JOI.2010.01.002 2010  53 251
2010 - 138 GONZALEZ-PEREIRA B, 2010, J INFORMETR 10.1016/J.JOI.2010.03.002 2010  23 181
2010 - 142           OPTHOF T, 2010, J INFORMETR 10.1016/J.JOI.2010.02.003 2010  38  99
2010 - 143       VAN RAAN AFJ, 2010, J INFORMETR 10.1016/J.JOI.2010.03.008 2010  25  65
2011 - 174         BORNMANN L, 2011, J INFORMETR 10.1016/J.JOI.2011.05.005 2011  39  35
2011 - 178          WALTMAN L, 2011, J INFORMETR 10.1016/J.JOI.2011.05.003 2011  58  27
2011 - 205      LEYDESDORFF L, 2011, J INFORMETR 10.1016/J.JOI.2011.01.008 2011  21  30
2012 - 238           ABRAMO G, 2012, J INFORMETR 10.1016/J.JOI.2012.03.005 2012  21  34
2012 - 242         BORNMANN L, 2012, J INFORMETR 10.1016/J.JOI.2012.04.002 2012  30   4
2013 - 317           ABRAMO G, 2013, J INFORMETR 10.1016/J.JOI.2013.07.002 2013  27  29
2013 - 319          WALTMAN L, 2013, J INFORMETR 10.1016/J.JOI.2013.08.002 2013  25  42
2013 - 329         BORNMANN L, 2013, J INFORMETR 10.1016/J.JOI.2013.09.003 2013  41  21
2016 - 625          WALTMAN L, 2016, J INFORMETR 10.1016/J.JOI.2016.02.007 2016  21  71

Thematic Map