Bibliometrics Analysis

Query

  • Sample: 499 document (2020/jan - 1997)
  • Source: https://www.scopus.com/
  • Scopus - Advanced search
  • Query: TITLE-ABS-KEY(“sustainable energy” OR “renewable energy”) AND TITLE-ABS-KEY(“machine learning”):
Legend: Renewable or Sustainable Energy and Machine Learning

Legend: Renewable or Sustainable Energy and Machine Learning

Install and load package if require:

if(!require("install.load")) {
  install.packages("install.load")
  library(install.load)
}
## Loading required package: install.load
install_load("dplyr","factoextra", "FactoMineR", "ggplot2", "igraph", "Matrix", "rscopus",
             "SnowballC", "stringr", "bibliometrix")
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
## Loading required package: ggplot2
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
## 
## Attaching package: 'igraph'
## The following objects are masked from 'package:dplyr':
## 
##     as_data_frame, groups, union
## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum
## The following object is masked from 'package:base':
## 
##     union
## 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
## 
##                         
## To start with the shiny web-interface, please digit:
## biblioshiny()

Preparing dataset:

# Data loading
D <- readFiles("https://julialang.com.br/wp-content/uploads/2020/03/renewable_energy.bib")
head(D)
## [1] ""                                                                                  
## [2] "@CONFERENCE{Brody1997182,"                                                         
## [3] "author={Brody, A.W. and Boyd, E. and Olmsted, C.},"                                
## [4] "title={Generating high-resolution data using hints},"                              
## [5] "journal={Proceedings of SPIE - The International Society for Optical Engineering},"
## [6] "year={1997},"
# Data converting
M <- convert2df(D, dbsource = "scopus", format = "bibtex")
## 
## Converting your scopus collection into a bibliographic dataframe
## 
## Articles extracted   100 
## Articles extracted   200 
## Articles extracted   300 
## Articles extracted   400 
## Articles extracted   499 
## Done!
## 
## 
## Generating affiliation field tag AU_UN from C1:  Done!

Bibliometrics Analysis:

# Results
results <- biblioAnalysis(M, sep = ";")
S <- summary(object = results, k = 10, pause = FALSE) # summary results
## 
## 
## Main Information about data
## 
##  Documents                             499 
##  Sources (Journals, Books, etc.)       316 
##  Keywords Plus (ID)                    3190 
##  Author's Keywords (DE)                1333 
##  Period                                1997 - 2020 
##  Average citations per documents       6.04 
## 
##  Authors                               1562 
##  Author Appearances                    1844 
##  Authors of single-authored documents  22 
##  Authors of multi-authored documents   1540 
##  Single-authored documents             37 
## 
##  Documents per Author                  0.319 
##  Authors per Document                  3.13 
##  Co-Authors per Documents              3.7 
##  Collaboration Index                   3.33 
##  
##  Document types                     
##  ARTICLE                216 
##  ARTICLE IN PRESS       4 
##  BOOK                   1 
##  BOOK CHAPTER           11 
##  CONFERENCE PAPER       215 
##  CONFERENCE REVIEW      16 
##  DATA PAPER             2 
##  EDITORIAL              1 
##  NOTE                   1 
##  REVIEW                 31 
##  SHORT SURVEY           1 
##  
## 
## Annual Scientific Production
## 
##  Year    Articles
##     1997        1
##     1999        1
##     2008        1
##     2009        3
##     2010        4
##     2011        3
##     2012        1
##     2013        8
##     2014       20
##     2015       23
##     2016       31
##     2017       52
##     2018       99
##     2019      220
##     2020       32
## 
## Annual Percentage Growth Rate 16.26294 
## 
## 
## Most Productive Authors
## 
##    Authors        Articles Authors        Articles Fractionalized
## 1  NA NA                16 NA NA                            16.00
## 2  DEO RC                7 JOSHUVA A                         2.23
## 3  JOSHUVA A             6 SCARTEZZINI JL                    1.83
## 4  SCARTEZZINI JL        6 SUGUMARAN V                       1.83
## 5  WANG Y                6 DEO RC                            1.59
## 6  ZHANG J               6 ASSOULINE D                       1.58
## 7  ASSOULINE D           5 MOHAJERI N                        1.58
## 8  MOHAJERI N            5 WANG Y                            1.57
## 9  SALCEDO-SANZ S        5 KAKKAR A                          1.50
## 10 ZHANG Y               5 SHARMA A                          1.50
## 
## 
## Top manuscripts per citations
## 
##                                    Paper           TC TCperYear
## 1  CHAOUACHI A, 2013, IEEE TRANS IND ELECTRON     359     44.88
## 2  GOLESTANEH F, 2016, IEEE TRANS POWER SYST       83     16.60
## 3  DAS UK, 2018, RENEWABLE SUSTAINABLE ENERGY REV  80     26.67
## 4  TABOR DP, 2018, NAT REV MATER                   80     26.67
## 5  HU W, 2009, ACM TRANS SENS NETW                 76      6.33
## 6  ZHANG Y, 2016, ENERGY CONVERS MANAGE            76     15.20
## 7  DERVILIS N, 2014, J SOUND VIB                   69      9.86
## 8  ARDABILI SF, 2018, ENG APPL COMPUT FLUID MECH   64     21.33
## 9  YEH WC, 2014, INT J ELECTR POWER ENERGY SYST    58      8.29
## 10 ASSOULINE D, 2017, SOL ENERGY                   56     14.00
## 
## 
## Corresponding Author's Countries
## 
##           Country Articles   Freq SCP MCP MCP_Ratio
## 1  USA                  46 0.1570  39   7     0.152
## 2  INDIA                29 0.0990  27   2     0.069
## 3  GERMANY              26 0.0887  23   3     0.115
## 4  CHINA                18 0.0614  18   0     0.000
## 5  SPAIN                13 0.0444  10   3     0.231
## 6  JAPAN                11 0.0375   9   2     0.182
## 7  KOREA                11 0.0375   9   2     0.182
## 8  UNITED KINGDOM       10 0.0341   7   3     0.300
## 9  HONG KONG             9 0.0307   3   6     0.667
## 10 ITALY                 9 0.0307   6   3     0.333
## 
## 
## SCP: Single Country Publications
## 
## MCP: Multiple Country Publications
## 
## 
## Total Citations per Country
## 
##      Country      Total Citations Average Article Citations
## 1  JAPAN                      379                    34.455
## 2  USA                        228                     4.957
## 3  UNITED KINGDOM             131                    13.100
## 4  SPAIN                       94                     7.231
## 5  INDIA                       88                     3.034
## 6  GERMANY                     85                     3.269
## 7  TAIWAN                      63                    21.000
## 8  CHINA                       58                     3.222
## 9  BRAZIL                      57                    19.000
## 10 SWITZERLAND                 57                    11.400
## 
## 
## Most Relevant Sources
## 
##                                                                                                                          Sources       
## 1  ENERGIES                                                                                                                            
## 2  APPLIED ENERGY                                                                                                                      
## 3  LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS)
## 4  RENEWABLE AND SUSTAINABLE ENERGY REVIEWS                                                                                            
## 5  APPLIED SCIENCES (SWITZERLAND)                                                                                                      
## 6  ENERGY CONVERSION AND MANAGEMENT                                                                                                    
## 7  IEEE ACCESS                                                                                                                         
## 8  ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING                                                                                       
## 9  ENERGY                                                                                                                              
## 10 SOLAR ENERGY                                                                                                                        
##    Articles
## 1        19
## 2        12
## 3        10
## 4         9
## 5         8
## 6         7
## 7         7
## 8         6
## 9         6
## 10        6
## 
## 
## Most Relevant Keywords
## 
##     Author Keywords (DE)      Articles               Keywords-Plus (ID)     Articles
## 1  MACHINE LEARNING                172 LEARNING SYSTEMS                          257
## 2  RENEWABLE ENERGY                 71 MACHINE LEARNING                          197
## 3  SMART GRID                       30 RENEWABLE ENERGY RESOURCES                150
## 4  FORECASTING                      25 FORECASTING                               141
## 5  ARTIFICIAL INTELLIGENCE          22 RENEWABLE ENERGIES                        125
## 6  DEEP LEARNING                    20 ARTIFICIAL INTELLIGENCE                   107
## 7  REINFORCEMENT LEARNING           20 LEARNING ALGORITHMS                        89
## 8  ARTIFICIAL NEURAL NETWORKS       16 SOLAR ENERGY                               79
## 9  MICROGRID                        15 WIND POWER                                 77
## 10 SUPPORT VECTOR REGRESSION        12 ELECTRIC POWER TRANSMISSION NETWORKS       73
plot(x = results, k = 10, pause = FALSE)              # plot results

# Analysis of cited Rederences
M$CR[1]
## [1] "TUCKER, G., SLINGERLAND, R., PREDICTIVE SEDIMENT FLUX FROM FOLD AND THRUST BELTS (1995) BASIN RESEARCH SPECIAL VOLUME ON TECTONIC GEOMORPHOLOGY; WESSER, A., DEBEVEC, ARCTIC-STEPPE DISTRIBUTION THROUGH SPACE AND TIME: A MICROCLIMATE MODELING APPROACH (1995) BRIDGES OF SCIENCE; BOYD, E., BRODY, A., PICTURE ANALYSIS BY ADAPTIVE ALGORITHMS (1997) PROCEEDINGS OF ELECTRONIC IMAGING: SCIENCE AND TECHNOLOGY"
CR1 <- citations(M, field = "article", sep = ";")     # To obtain the most frequent cited manuscripts
cbind(CR1$Cited[1:10])
##                                                                                                                                                                                                                                                                        [,1]
## BREIMAN, L., RANDOM FORESTS (2001) MACHINE LEARNING, 45 (1), PP. 5-32                                                                                                                                                                                                    10
## MIT PRESS: CAMBRIDGE, MA, USA                                                                                                                                                                                                                                             6
## VAPNIK, V., (1995) THE NATURE OF STATISTICAL LEARNING THEORY, , SPRINGER, NEW YORK                                                                                                                                                                                        6
## BACHER, P., MADSEN, H., NIELSEN, H.A., ONLINE SHORT-TERM SOLAR POWER FORECASTING (2009) SOLAR ENERGY, 83 (10), PP. 1772-1783                                                                                                                                              5
## DEO, R.C., WEN, X., QI, F., A WAVELET-COUPLED SUPPORT VECTOR MACHINE MODEL FOR FORECASTING GLOBAL INCIDENT SOLAR RADIATION USING LIMITED METEOROLOGICAL DATASET (2016) APPL ENERGY, 168, PP. 568-593                                                                      5
## HINTON, G.E., SALAKHUTDINOV, R.R., REDUCING THE DIMENSIONALITY OF DATA WITH NEURAL NETWORKS (2006) SCIENCE, 313 (5786), PP. 504-507                                                                                                                                       5
## HOCHREITER, S., SCHMIDHUBER, J., LONG SHORT-TERM MEMORY (1997) NEURAL COMPUTATION, 9 (8), PP. 1735-1780                                                                                                                                                                   5
## JOSHUVA, A., SUGUMARAN, V., AMARNATH, M., SELECTING KERNEL FUNCTION OF SUPPORT VECTOR MACHINE FOR FAULT DIAGNOSIS OF ROLLER BEARINGS USING SOUND SIGNALS THROUGH HISTOGRAM FEATURES (2015) INTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, 10 (68), PP. 482-487    5
## SPRINGER BERLIN HEIDELBERG: BERLIN/HEIDELBERG, GERMANY                                                                                                                                                                                                                    5
## , PP. 1-6                                                                                                                                                                                                                                                                 4
CR2 <- citations(M, field = "author", sep = ";")      # To obtain the most frequent cited first authors
cbind(CR2$Cited[1:10])
##                [,1]
## WANG Y          152
## WANG J          148
## LIU Y           146
## LI Y            144
## ZHANG J         127
## ZHANG Y         102
## CHEN Y           88
## DEO R C          87
## SALCEDO SANZ S   80
## WANG X           79
# This works only with wos data for now
# CR3 <- localCitations(M, sep = ";")                   # To obtain the most frequent local cited authors
# CR3$Authors[1:10,]
# CR3$Papers[1:10,]
# Authors' Dominance ranking
DF <- dominance(results, k = 10)
DF
##            Author Dominance Factor Tot Articles Single-Authored Multi-Authored First-Authored Rank by Articles
## 1       JOSHUVA A        1.0000000            6               0              6              6                8
## 2     ASSOULINE D        1.0000000            5               0              5              5                5
## 3         ZHANG Y        0.8000000            5               0              5              4                5
## 4  SALCEDO-SANZ S        0.6000000            5               0              5              3                5
## 5          WANG Y        0.5000000            6               0              6              3                8
## 6          WANG B        0.5000000            4               0              4              2                1
## 7         HUANG H        0.2500000            4               0              4              1                1
## 8            LI J        0.2500000            4               0              4              1                1
## 9            LU S        0.2500000            4               0              4              1                1
## 10        ZHANG J        0.1666667            6               0              6              1                8
##    Rank by DF
## 1           1
## 2           1
## 3           3
## 4           4
## 5           5
## 6           5
## 7           7
## 8           7
## 9           7
## 10         10
# h-index 10 authors
authors=gsub(","," ",names(results$Authors)[1:10])
indices <- Hindex(M, field = "author", elements=authors, sep = ";", years = 50)
indices$H
##            Author h_index g_index    m_index  TC NP PY_start
## 1           NA NA       1       1 0.09090909   1 16     2010
## 2          DEO RC       4       7 1.33333333 124  7     2018
## 3       JOSHUVA A       3       6 0.60000000  68  6     2016
## 4  SCARTEZZINI JL       3       6 0.75000000  91  6     2017
## 5          WANG Y       2       4 0.50000000  19  6     2017
## 6         ZHANG J       2       5 0.33333333  31  6     2015
## 7     ASSOULINE D       3       5 0.75000000  91  5     2017
## 8      MOHAJERI N       3       5 0.75000000  91  5     2017
## 9  SALCEDO-SANZ S       4       5 0.57142857 116  5     2014
## 10        ZHANG Y       2       5 0.40000000  87  5     2016
# Author' h-index
indices <- Hindex(M, field = "author", elements="ZHANG Y", sep = ";", years = 50) # need to change authors name
# ZHANG Y's impact indices:
indices$H
##    Author h_index g_index m_index TC NP PY_start
## 1 ZHANG Y       2       5     0.4 87  5     2016
# ZHANG Y's citations
indices$CitationList
## [[1]]
##                          Authors                        Journal Year TotalCitation
## 3     ZHANG Y;WEI Y;GUO D;SONG M LECTURE NOTES IN COMPUTER SCIE 2019             0
## 4 ZHANG Y;TANG H;WANG K;PAN Y;LI KONGZHI LILUN YU YINGYONG/CONT 2019             0
## 5 DU M;LI Y;WANG B;ZHANG Y;LUO P ZHONGGUO DIANJI GONGCHENG XUEB 2019             1
## 2 ZHANG Y;YANG R;ZHANG K;JIANG H       IEEE INTELLIGENT SYSTEMS 2017            10
## 1       ZHANG Y;LIU K;QIN L;AN X ENERGY CONVERSION AND MANAGEME 2016            76

Estimation

# Lotkas Law coefficient estimation
L <- lotka(results)
# Author Productivity. Empirical Distribution
L$AuthorProd
##   N.Articles N.Authors         Freq
## 1          1      1368 0.8758002561
## 2          2       152 0.0973111396
## 3          3        23 0.0147247119
## 4          4         9 0.0057618438
## 5          5         4 0.0025608195
## 6          6         4 0.0025608195
## 7          7         1 0.0006402049
## 8         16         1 0.0006402049
lokta_table <- matrix(c(L$Beta, L$C, L$R2, L$p.value), ncol = 1, byrow = TRUE)
colnames(lokta_table) <- "Estimation"
rownames(lokta_table) <- c("Beta: ", "Constant: ", "Goodness of fit: ", "P-value: ")
lokta_table <- as.table(lokta_table)
print(lokta_table)
##                   Estimation
## Beta:              2.8507849
## Constant:          0.4565744
## Goodness of fit:   0.9079044
## P-value:           0.2699997

Compare the two distributions using plot function:

# Observed distribution
Observed=L$AuthorProd[,3]
# Theoretical distribution with Beta = 2
Theoretical=10^(log10(L$C)-2*log10(L$AuthorProd[,1]))

Plots

plot(L$AuthorProd[,1],Theoretical,type="l",col="red",ylim=c(0, 1), xlab="Articles",ylab="Freq. of Authors",main="Scientific Productivity")
lines(L$AuthorProd[,1],Observed,col="blue")
legend(x="topright",c("Theoretical (B=2)","Observed"),col=c("red","blue"),lty = c(1,1,1),cex=0.6,bty="n")

# Bibliographic coupling
NetMatrix <- biblioNetwork(M, analysis = "coupling", network = "authors", sep = ";")
# plot authors' similarity (first 20 authors), using salton similarity index
net <- networkPlot(NetMatrix,  normalize = "salton", weighted=NULL, n = 100, Title = "Authors' Coupling", type = "fruchterman", size=5,size.cex=T,remove.multiple=TRUE,labelsize=0.8,label.n=10,label.cex=F)

# Bibliographic co-citation
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ".  ")
net <- networkPlot(NetMatrix, n = 20, type = "kamada", Title = "co-citation", labelsize = 1.0)

# Bibliographic collaboration
# authors' collaboration network:
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "authors", sep = ";")
net <- networkPlot(NetMatrix, n = 20, type = "kamada", Title = "Author collaboration", labelsize = 1.0)

# Create a county collaboration network
M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")
# Plot the network
net <- networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country Collaboration", type = 'circle', size = TRUE, remove.multiple = FALSE, labelsize = 0.8)

# Keyword co-occurrences
# Create keyword co-occurrencies network
NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
# Plot the network
net <- networkPlot(NetMatrix, normalize="association", weighted=T, n = 30, Title = "Keyword Co-occurrences", type = "fruchterman", size=T,edgesize = 5,labelsize=0.7)

Co-word analysis:

# Conceptual Structure using keywords
CS <- conceptualStructure(M,field="DE_TM", minDegree = 5, k.max = 5, stemming = FALSE, labelsize = 9)

CS <- conceptualStructure(M,field="ID", method="CA", minDegree=4, clust=5, stemming=FALSE, labelsize=10, documents=10)

# Create a historical citation network
# histResults <- histNetwork(M, n = 20, sep = ".  ") # works with wos data only
# Plot a historical co-citation network
# net <- histPlot(histResults, size = FALSE, label = TRUE, arrowsize = 0.5) # works with wos data only
# AuthorProdOverTime, fig.height=6, fig.width=8
topAU <- authorProdOverTime(M, k = 10, graph = TRUE)

# Table: Author's productivity per year
# AuthorProdOverTime, fig.height=6, fig.width=8
head(topAU$dfAU)
##        Author year freq  TC      TCpY
## 1 ASSOULINE D 2017    2  62 15.500000
## 2 ASSOULINE D 2018    2  29  9.666667
## 3 ASSOULINE D 2019    1   0  0.000000
## 4      DEO RC 2018    3 119 39.666667
## 5      DEO RC 2019    2   5  2.500000
## 6      DEO RC 2020    2   0  0.000000
# Table: Auhtor's documents list
#head(topAU$dfPapersAU)

Bipartite network

A <- cocMatrix(M, Field = "SO", sep = ";")
# Most relevant sources
sort(Matrix::colSums(A), decreasing = TRUE)[1:5]
##                                                                                                                             ENERGIES 
##                                                                                                                                   19 
##                                                                                                                       APPLIED ENERGY 
##                                                                                                                                   12 
## LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS) 
##                                                                                                                                   10 
##                                                                                             RENEWABLE AND SUSTAINABLE ENERGY REVIEWS 
##                                                                                                                                    9 
##                                                                                                       APPLIED SCIENCES (SWITZERLAND) 
##                                                                                                                                    8
# A <- cocMatrix(M, Field = "CR", sep = ".  ")
# A <- cocMatrix(M, Field = "AU", sep = ";")
M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
# A <- cocMatrix(M, Field = "AU_CO", sep = ";")
# A <- cocMatrix(M, Field = "DE", sep = ";")
# A <- cocMatrix(M, Field = "ID", sep = ";")
# NetMatrix <- biblioNetwork(M, analysis = "coupling", network = "references", sep = ".  ")
# similarity, fig.height=9, fig.width=9, warning=FALSE
NetMatrix <- biblioNetwork(M, analysis = "coupling", network = "authors", sep = ";")
net=networkPlot(NetMatrix,  normalize = "salton", weighted=NULL, n = 100, Title = "Authors' Coupling", type = "fruchterman", size=5,size.cex=T,remove.multiple=TRUE,labelsize=0.8,label.n=10,label.cex=F)

# NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ".  ")
# NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "authors", sep = ";")
# NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")
# An example of a classical keyword co-occurrences network
NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
netstat <- networkStat(NetMatrix)
names(netstat$network)
##  [1] "networkSize"             "networkDensity"          "networkTransitivity"     "networkDiameter"        
##  [5] "networkDegreeDist"       "networkCentrDegree"      "networkCentrCloseness"   "networkCentrEigen"      
##  [9] "networkCentrbetweenness" "NetworkAverPathLeng"
names(netstat$vertex)
## NULL
summary(netstat, k=10)
## 
## 
## Main statistics about the network
## 
##  Size                                  3197 
##  Density                               0.013 
##  Transitivity                          0.142 
##  Diameter                              3 
##  Degree Centralization                 0.696 
##  Average path length                   2.123 
## 
# Country collaboration, fig.height=7, fig.width=7, warning=FALSE
# Create a country collaboration network
M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")
# Plot the network
net=networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country Collaboration", type = "circle", size=TRUE, remove.multiple=FALSE,labelsize=0.7,cluster="none")

# Co-citation network, fig.height=7, fig.width=7, warning=FALSE
# Create a co-citation network
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
# Plot the network
net=networkPlot(NetMatrix, n = 30, Title = "Co-Citation Network", type = "fruchterman", size=T, remove.multiple=FALSE, labelsize=0.7,edgesize = 5)

## ----Keyword c-occurrences, fig.height=7, fig.width=7, warning=FALSE--------------------------------------------------
# Create keyword co-occurrences network
NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
# Plot the network
net=networkPlot(NetMatrix, normalize="association", weighted=T, n = 30, Title = "Keyword Co-occurrences", type = "fruchterman", size=T,edgesize = 5,labelsize=0.7)

# Historical Co-citation network, fig.height=7, fig.width=10, warning=FALSE
# Create a historical citation network
# options(width=130)
# histResults <- histNetwork(M, min.citations = 10, sep = ";")
# Plot a historical co-citation network
# net <- histPlot(histResults, n=15, size = 10, labelsize=5)