June 10, 2023

Read and rename raw data.

> wdir <- "C:/test/Natalija/data"
> setwd(wdir)
> SF <- read.csv("scopus_Kaist_rawdata.csv",header=TRUE)
> short <- c("au", "IDa", "tit", "y", "jour", "vol", "iss", "art", "beg", 
+ "end", "np", "cite", "DOI", "URL", "type", "ver", "acc", "src", "IDe")  
> colnames(SF) <- short
> str(SF)
 
'data.frame':   12543 obs. of  19 variables:
 $ au  : chr  "Ryu, S., Wang, J.E., Kim, J.-H., Ruffo, R., Jung, Y.H., Kim, D.K." "Lee, B.-Y., Kim, D.H., Park, J.,  ...
 $ IDa : chr  "57193059482; 56913501600; 57865565300; 6602426816; 22979935700; 55574223813" "56518539100; 55574225422; ...
 $ tit : chr  "A study on cobalt substitution in sodium manganese mixed-anion phosphates as positive electrode materials ...
 $ y   : int  2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 ...
 $ jour: chr  "Journal of Power Sources" "Science and Technology of Advanced Materials" "Journal of Web Engineering" ...
 $ vol : chr  "444" "20" "18" "229" ...
 $ iss : chr  "" "1" "8" "" ...
 $ art : chr  " 227274" "" "" " 116797" ...
 $ beg : chr  "" "758" "837" "" ...
 $ end : chr  "" "773" "864" "" ...
 $ np  : logi  NA NA NA NA NA NA ...
 $ cite: int  7 71 NA 16 5 25 6 8 18 10 ...
 $ DOI : chr  "10.1016/j.jpowsour.2019.227274" "10.1080/14686996.2019.1631716" "10.13052/jwe1540-9589.1884" ...
 $ URL : chr  "https://www.scopus.com/inward/record.url?eid=2-s2.0-85073156220&partnerID=40&md5=428f55ce083bb7" ... 
 $ type: chr  "Article" "Review" "Article" "Article" ...
 $ ver : chr  "Final" "Final" "Final" "Final" ...
 $ acc : chr  "" "All Open Access, Gold, Green" "" "" ...
 $ src : chr  "Scopus" "Scopus" "Scopus" "Scopus" ...
 $ IDe : chr  "2-s2.0-85073156220" "2-s2.0-85068763946" "2-s2.0-85097943650" "2-s2.0-85071928006" ...
> dim(SF)
[1] 12543    19

Split info about authors

> n <- nrow(SF); C <- rep(NA,9*n); N <- length(C)
> A <- data.frame(work=C,IDa=C,au=C,y=C)
> k <- 0
> for(w in 1:n){
+   ans <- gsub("\\.","",strsplit(SF$au[w],"\\., ")[[1]])
+   ais <- strsplit(SF$IDa[w],"; ")[[1]]
+   if(length(ans)==length(ais)) {
+     for(i in 1:length(ans)){ k <- k+1 
+       if(k > N) stop(paste("increase size, w=",w))
+       A$work[k] <- w; A$IDa[k] <- ais[i]; A$au[k] <- ans[i]; A$y[k] <- SF$y[w] 
+     }
+   } else warning(paste("work ",w))
+ }
Warning messages:
1: work  213 
2: work  319 
3: work  390 
4: work  4813 
5: work  8499 
6: work  9249 

The above code can be improved by adding some tracing

> n <- nrow(SF); C <- rep(NA,15*n); N <- length(C); step <- 5000
> A <- data.frame(work=C,IDa=C,au=C)
> cat("Start",date(),", n =",n,"\n")
> k <- 0
> for(w in 1:n){
+   ans <- gsub("\\.","",strsplit(SF$au[w],"\\., ")[[1]])
+   ais <- strsplit(SF$IDa[w],"; ")[[1]]
+   if(length(ans)==length(ais)) {
+     for(i in 1:length(ans)){ k <- k+1
+       if(k %% step==0) cat(date()," w =",w,"  k =",k," k/w =",k/w,"\n"); flush.console() 
+       if(k > N) stop(paste("increase size, w=",w))
+       A$work[k] <- w; A$IDa[k] <- ais[i]; A$au[k] <- ans[i]
+     }
+   } else warning(paste("work ",w))
+ }
> cat("Stop",date(),", k =",k,", k/n =",k/n,"\n")

Start Sun Jun 18 00:06:54 2023 , n = 12543 
Sun Jun 18 00:07:07 2023  w = 807   k = 5000  k/w = 6.195787 
Sun Jun 18 00:07:21 2023  w = 1762   k = 10000  k/w = 5.675369 
Sun Jun 18 00:07:34 2023  w = 2684   k = 15000  k/w = 5.588674 
Sun Jun 18 00:07:48 2023  w = 3720   k = 20000  k/w = 5.376344 
Sun Jun 18 00:08:01 2023  w = 4203   k = 25000  k/w = 5.948132 
Sun Jun 18 00:08:15 2023  w = 4856   k = 30000  k/w = 6.177924 
Sun Jun 18 00:08:29 2023  w = 5565   k = 35000  k/w = 6.289308 
Sun Jun 18 00:08:43 2023  w = 5811   k = 40000  k/w = 6.883497 
Sun Jun 18 00:08:57 2023  w = 6023   k = 45000  k/w = 7.47136 
Sun Jun 18 00:09:11 2023  w = 6213   k = 50000  k/w = 8.047642 
Sun Jun 18 00:09:25 2023  w = 6522   k = 55000  k/w = 8.432996 
Sun Jun 18 00:09:39 2023  w = 7240   k = 60000  k/w = 8.287293 
Sun Jun 18 00:09:52 2023  w = 7448   k = 65000  k/w = 8.727175 
Sun Jun 18 00:10:06 2023  w = 8074   k = 70000  k/w = 8.669804 
Sun Jun 18 00:10:20 2023  w = 8397   k = 75000  k/w = 8.931761 
Sun Jun 18 00:10:34 2023  w = 8977   k = 80000  k/w = 8.911663 
Sun Jun 18 00:10:49 2023  w = 9956   k = 85000  k/w = 8.537565 
Sun Jun 18 00:11:03 2023  w = 10714   k = 90000  k/w = 8.400224 
Sun Jun 18 00:11:18 2023  w = 11717   k = 95000  k/w = 8.107877 
Warning messages:
1: work  213 
2: work  319 
3: work  390 
4: work  4813 
5: work  8499 
6: work  9249 
Stop Sun Jun 18 00:11:29 2023 , k = 98987 , k/n = 7.891812  

In the command C ← rep(NA,15*n) the number 15 is a guess about the average number of authors per paper. If the real average is larger the program will stop with the increase size message. A good approximation of the real average is the last k/w. Replace 15 with 1.3 * k/w and rerun the program. The value of the variable step controls the frequency of the trace lines.

There are some rows with different numbers of names and IDs.

Inspecting the raw data in these rows using the code (changing w)

> w <- 9249
> (SF$tit[w])
> (SF$au[w])
> (ans <- gsub("\\.","",strsplit(SF$au[w],"\\., ")[[1]]))
> (ais <- strsplit(SF$IDa[w],"; ")[[1]])

it turns out that the troublemakers are single-word names. Row 9249 describes an article with 1271 coauthors.

We correct the data (adding a dot to the end of single-word names) and rerun the splitting code.

> SF$au[213] <- "Park, J., De Liu., Yi, M.Y., Santhanam, R."
> SF$au[319] <- "Datt, R., Suman., Bagui, A., Siddiqui, A., Sharma, R., Gupta, V., Yoo, S., Kumar, S., Singh, S.P."
> SF$au[390] <- "Fazl-i-Sattar., Ahmed, A., Ullah, H., Ullah, Z., Tariq, M., Ayub, K."
> SF$au[4813] <- "Samaad, T., Tahir, G.A., Mansoor-Ur-Rahman., Ashraf, M."
> SF$au[8499] <- "Park, H.C., Isnaeni., Gong, S., Cho, Y.-H."
> SF$au[9249] <- gsub("Pisano","Pisano.",SF$au[9249])
> 
> n <- nrow(SF); C <- rep(NA,9*n); N <- length(C)
> A <- data.frame(work=C,IDa=C,au=C,y=C)
> k <- 0
> for(w in 1:n){
+   ans <- gsub("\\.","",strsplit(SF$au[w],"\\., ")[[1]])
+   ais <- strsplit(SF$IDa[w],"; ")[[1]]
+   if(length(ans)==length(ais)) {
+     for(i in 1:length(ans)){ k <- k+1 
+       if(k > N) stop(paste("increase size, w=",w))
+       A$work[k] <- w; A$IDa[k] <- ais[i]; A$au[k] <- ans[i]; A$y[k] <- SF$y[w] 
+     }
+   } else warning(paste("work ",w))
+ }
> k
[1] 100285
> A <- A[1:k,]
> dim(A)
[1] 100285      4

> Wd <- table(table(A$work))
> head(Wd)

   1    2    3    4    5    6 
 231 1916 2593 2270 1617 1075 
> length(Wd)
[1] 65
> tail(Wd)

1233 1235 1488 1497 1551 1558 
   1    1    1    1    1    1 
> x <- as.numeric(names(Wd))
> plot(x,Wd,log="xy",pch=16,cex=0.7,xlab="#authors",ylab="freq",
+   main="Kaist - #authors per paper distribution")
>
> Id <- table(table(A$IDa))
> head(Id)

    1     2     3     4     5     6 
16609  4108  2121  1108   661   496 
> tail(Id)

 80  86  93  96 103 105 
  2   1   1   1   1   1 
> x <- as.numeric(names(Id))
> plot(x,Id,log="xy",pch=16,cex=0.7,xlab="#works",ylab="freq",
+   main="Kaist - #works per author distribution")

There are 2270 works with 4 authors and a single work with 1558 authors. 16609 authors contributed to a single work and there is an author that contributed to 105 works.

We attach to the author IDs the corresponding names.

> I <- factor(A$IDa); L <- levels(I); Ia <- as.integer(I)
> nA <- length(L)
> nA
[1] 28053
> Anam <- rep(NA,nA); names(Anam) <- L
> for(i in 1:nrow(A)){ nam <- A$au[i]; ID <- A$IDa[i]
+   if(is.na(Anam[ID])) Anam[ID] <- nam else
+   if(Anam[ID]!=nam) warning(paste(ID,":",nam,"/",Anam[ID])) 
+ }
 
Warning messages:
1: 56298144400 : Yoo, KW / Yoo, K
2: 7202588575 : Chun, JH / Chun, J
3: 55182524000 : Choi, BG / Choi, B-G
4: 56298144400 : Yoo, KW / Yoo, K
5: 7202588575 : Chun, JH / Chun, J
6: 55182524000 : Choi, BG / Choi, B-G
7: 57205232256 : Kim, SH / Kim, S
8: 36965693000 : Kyhm, JH / Kyhm, J
9: 57206099583 : Kim, DJ / Kim, D
10: 57205232256 : Kim, S-H / Kim, S
11: 56178406700 : Lim, H-K / Lim, HK
12: 14056089600 : Lee, H-U / Lee, HU
13: 55494376500 : Park, Y-K / Park, YK
14: 56178406700 : Lim, H-K / Lim, HK
15: 56377217300 : Park, J / Park, JC
16: 36473564000 : Lee, DJ / Lee, D-J
17: 6602354436 : Do Heo, W / Heo, WD
18: 6602354436 : Do Heo, W / Heo, WD
19: 25634053100 : Lee, S / Lee, SB
20: 57221622000 : Lee, J / Lee, J-S
21: 8242246500 : Kim, S-C / Kim, SC
22: 7404469270 : Cho, Y / Cho, Y-H
23: 55928867600 : Lee, S-W / Lee, SW
24: 55857721300 : Yoo, D-E / Yoo, DE
25: 57211666172 : Shim, JE / Shim, J-E
26: 57197718957 : Seo, J / Seo, JH
27: 56949819000 : Cha, SL / Cha, S-L
28: 55807779600 : Han, J-H / Han, J
29: 7404786542 : Song, JD / Song, J
30: 57205232256 : Kim, S-H / Kim, S
31: 57204616683 : Jeon, G / Jeon, G-J
32: 57204766033 : Kim, MW / Kim, M-W
33: 6602354436 : Do Heo, W / Heo, WD
34: 47861080000 : Kim, J-H / Kim, J
35: 57206099583 : Kim, DJ / Kim, D
36: 57218106364 : Kim, D-H / Kim, D
37: 57202965763 : Kim, KM / Min Kim, K
38: 55523269400 : Hong, SC / Hong, S-C
39: 8549444100 : Lee, JR / Lee, J-R
40: 55928816500 : Park, H / Park, HK
41: 57200220938 : Lee, S / Lee, S-H
42: 57203690087 : Hwang, H / Hwang, HS
43: 57192500386 : Lee, HJ / Lee, H
44: 57189594982 : Sasikala, SP / Padmajan Sasikala, S
45: 57193226486 : Hoang Bui, VK / Bui, VKH
46: 55549849900 : Park, Y / Park, Y-J
47: 7501843237 : Chang, Y-K / Chang, YK
48: 57193537793 : Kim, GY / Kim, G
49: 57199467150 : Lee, DW / Lee, D-W
50: 18535939800 : Kang, IS / Kang, I-S

We see that some authors appear in the data with different names.

This part could be improved by a selection of a kind of canonical name - remove ”-”, preference for longer initials, etc. ???

Now we are ready to save the transformed data as a Pajek two-mode network WA.net and additionally author names on the file authors.nam.

> net <- file("WA.net","w",encoding="UTF-8")
> RN <- SF$IDe; nr <- length(RN)
> CN <- L;  nc <- length(CN)
> U <- A$work; V <- Ia; w <- rep(1,length(U)); t <- A$y
> cat("% CSV2Pajek",date(),"\n*vertices",nr+nc,nr,"\n",file=net)   
> for(i in 1:nr) cat(i,' "',RN[i],'"\n',sep="",file=net)
> for(i in 1:nc) cat(i+nr,' "',CN[i],'"\n',sep="",file=net)
> cat("*arcs\n",file=net)
> for(i in 1:length(U)) cat(U[i],V[i]+nr,w[i],"[",t[i],"]\n",file=net)
> close(net)
> 
> nam <- file("authors.nam","w",encoding="UTF-8")
> cat("% CSV2Pajek",date(),"\n*vertices",nc,"\n",file=nam)
> for(i in 1:nc) cat(i,' "',Anam[i],'"\n',sep="",file=nam)
> close(nam)

Using authors.nam we can replace in Pajek the author IDs with the corresponding names and get more readable results.

The file authors.nam is encoded in UTF-8 but without BOM (signature) required by Pajek. I added it in EmEditor by saving it with encoding “UTF-8 with Signature”.

For creation and analysis of co-authorship networks see SocNet/Coauthor and macros.

For example strict co-authorship.

I added the “canonical” author names and exported the list in UTF-8-BOM encoding required by Pajek.

> stand <- function(s){
+   p <- strsplit(s,", ")[[1]]; k <- length(p)
+   if(k>1) p[k] <- gsub("-","",p[k]) else {p <- c(p,""); k <- 2}
+   return(list(k=k,p=p))
+ }
> 
> I <- factor(A$IDa); L <- levels(I); Ia <- as.integer(I)
> Anam <- rep(NA,length(L)); names(Anam) <- L
> for(i in 1:nrow(A)){ ID <- A$IDa[i]
+   b <- stand(A$au[i]); nam <- paste(b$p,collapse=", ")
+   if(is.na(Anam[ID])) Anam[ID] <- nam else 
+   if(Anam[ID]!=nam) {
+     a <- stand(Anam[ID])
+     na <- nchar(a$p[a$k]); nb <- nchar(b$p[b$k])
+     if(nb>na) { warning(paste(i,ID,":",A$au[i],"/",Anam[ID]))
+        Anam[ID] <- nam } 
+   }
+ }
> 
> nam <- file("authors2.nam","wb")
> writeBin(charToRaw('\xEF\xBB\xBF'),nam,endian="little")
> writeBin(charToRaw(paste("% CSV2Pajek",date(),"\n*vertices",nc,"\n")),nam,endian="little")
> for(i in 1:nc) writeBin(charToRaw(paste(i,' "',Anam[i],'"\n',sep="")),nam,endian="little")
> close(nam)

September 1, 2023

Nataliya prepared Pajek *.net files for eight universities. I computed the (all) degrees and saved them on *.clu files.

> F
             V1    V2    V3
1 WA_HKPoly.net 47165 15155
2 WA_HKUST1.net 37333  9225
3  WA_KAIST.net 40549 12543
4   WA_Maas.net 80334 13338
5    WA_NTY.net 82971 25192
6 WA_Pohang.net 21951  6179
7   WA_Pomp.net 36769  6054
8    WA_UTS.net 45329 13094

The following program produces plots of author and work distributions for all universities. It saves the data in the list R for later processing.

> wdir <- "C:/Users/vlado/docs/papers/2023/Natalija/data2/nets"
> setwd(wdir)
> F <- read.csv("files.dat",header=FALSE,sep="")
> R <- NULL
> for(i in 1:nrow(F)){
+   f <- gsub("net","clu",F[i,1])
+   nam <- gsub("WA_","",gsub(".net","",F[i,1]))
+   c <- read.csv(f,header=FALSE,skip=1,sep="")$V1
+   nw <- F$V3[i]; w <- c[1:nw]
+   a <- c[(nw+1):F$V2[i]]; na <- length(a)
+   cat("\n",i,nam,nw,na,"\n")
+   Wd <- table(w)
+   cat("authors\n"); print(head(Wd)); print(tail(Wd)) 
+   x <- as.numeric(names(Wd))
+   pdf(file=gsub(".net","-a.pdf",F[i,1]))
+   plot(x,Wd,log="xy",pch=16,cex=0.7,xlab="#authors",ylab="freq",
+     main=paste(nam,"- #authors per paper distribution"))
+   dev.off()
+   Ad <- table(a)
+   cat("works\n"); print(head(Ad)); print(tail(Ad)) 
+   x <- as.numeric(names(Ad))
+   pdf(file=gsub(".net","-x.pdf",F[i,1]))
+   plot(x,Ad,log="xy",pch=16,cex=0.7,xlab="#works",ylab="freq",
+     main=paste(nam,"- #works per author distribution"))
+   dev.off()
+   R <- append(R,list(nam=nam,nw=nw,na=na,Wd=Wd,Ad=Ad))
+ }

 1 HKPoly 15155 32010 
authors
w
   0    1    2    3    4    5 
   3  607 2065 2970 2848 2170 
w
 988  989 1018 1043 1159 1211 
   1    1    2    2    1    1 
works
a
    1     2     3     4     5     6 
19605  4826  2142  1213   823   539 
a
140 141 144 149 175 248 
  1   1   1   1   1   1 

 2 HKUST1 9225 28108 
authors
w
   0    1    2    3    4    5 
   2  212 1144 1635 1581 1286 
w
2949 2951 2952 2953 5096 5215 
   2    1    1    1    1    1 
works
a
    1     2     3     4     5     6 
14735  5111  1445   708   408   316 
a
 296  297  298  299  302  438 
  66 1421   17    3    1    1 

 3 KAIST 12543 28006 
authors
w
   0    1    2    3    4    5 
   1  231 1916 2594 2272 1618 
w
1227 1234 1484 1489 1545 1555 
   1    1    1    1    1    1 
works
a
    1     2     3     4     5     6 
16624  4105  2123  1108   660   498 
a
 80  86  93  96 103 105 
  2   1   1   1   1   1 

 4 Maas 13338 66996 
authors
w
   0    1    2    3    4    5 
  10  832 1164 1352 1372 1454 
w
1202 1261 1264 1265 1590 2582 
   1    1    1    1    1    1 
works
a
    1     2     3     4     5     6 
43452 10350  4822  2098  1382   932 
a
 94 100 102 104 120 122 
  1   1   1   1   1   1 

 5 NTY 25192 57779 
authors
w
   0    1    2    3    4    5 
   7 1331 3175 4299 4307 3500 
w
 988  989 1018 1043 1159 1211 
   1    1    2    2    1    1 
works
a
    1     2     3     4     5     6 
36525  8709  3790  2049  1333   950 
a
127 129 152 153 160 188 
  1   2   1   1   1   1 

 6 Pohang 6179 15772 
authors
w
  1   2   3   4   5   6 
 98 606 949 998 909 653 
w
 72  75 106 155 187 382 
  1   1   1   1   1   1 
works
a
    1     2     3     4     5     6 
10072  2454  1046   618   340   287 
a
 74  75  77  88  91 169 
  1   1   1   1   1   1 

 7 Pomp 6054 30715 
authors
w
  0   1   2   3   4   5 
  1 613 859 968 585 433 
w
 979  988 1018 1043 1159 1211 
   1    1    2    2    1    1 
works
a
    1     2     3     4     5     6 
19550  4995  1774  1028   718   493 
a
 76  79  81  85 114 117 
  1   1   1   1   1   1 

 8 UTS 13094 32235 
authors
w
   0    1    2    3    4    5 
   1  883 1510 2320 2430 1983 
w
 988  989 1018 1043 1159 1211 
   1    1    2    2    1    1 
works
a
    1     2     3     4     5     6 
20874  4878  1901  1024   721   445 
a
130 149 158 169 177 192 
  1   1   1   1   1   1 

The structure of NET files is the following

file: WA_Pomp.net

% CSV2Pajek Thu Jul 13 16:24:57 2023
*vertices 36769 6054
1 "2-s2.0-85077240176"                  IDs of works
2 "2-s2.0-85077129493"
...
6053 "2-s2.0-84941710968"
6054 "2-s2.0-84881717588"
6055 "10038927800"                      IDs of authors
6056 "10039034700"
6057 "10039087200"
...
36768 "9942846400"
36769 "9943079200"
*arcs
1 26773 1 [ 2019 ]                      arcs with a time stamp
1 18945 1 [ 2019 ]
...

The IDs are used because they uniquely identify the units - some of them can have the same name. To obtain the readable results the IDs have to be replaced with the corresponding names that are available in the NAM files.

file: authors_Pomp.nam

% CSV2Pajek Thu Jul 13 16:24:59 2023
*vertices 30715
1 "Kopisch-Obuch, FJ"
2 "Muro, D"
3 "Berking, M"
...
30713 "Langenberg, C"
30714 "Minov, J"
30715 "Aliaga, RS"

If you don't change IDs with names, they will be used in the results, and you can then use them in searching for additional information. To change IDs with names you have first to change IDs to the default labels

Network/Create new network/Transform/Add/Vertex labels/Default

and afterward, replace these with labels from the NAM file (select *.* (all files) to see them).

Network/Create new network/Transform/Add/Vertex labels/From file(s)

The works with a very large number of coauthors substantially increase the size of the corresponding collaboration networks but the contribution of a single author is very small. How to consider only the relevant authors? An option is truncated co-appearance networks.