Transcript Folie 1

Methods of Social Network
Analysis explained with help of
Collaboration Networks in
COLLNET
Hildrun Kretschmer
Department of Library and Information Science, Humboldt-University of Berlin,
Germany
E-mail: [email protected]
Abstract
•There is a rapid increase of network analysis in several scientific
disciplines beginning some decades ago. The social network
analysis (SNA) is developed especially in sociology and in social
psychology in collaboration with mathematics, statistics and
computer science.
Social network analysis (SNA) can also be used successfully in
the information sciences, as well as in studies of collaboration in
science. Several methods of social network analysis will be
explained with help of collaboration networks in COLLNET.
The growing importance of collaboration in research and the still
underdeveloped state-of-the-art of research on collaboration have
encouraged scientists from more than 20 countries to establish in
2000 a global interdisciplinary research network under the title
“Collaboration in Science and in Technology” (COLLNET) with
Berlin as its virtual centre.
The intention is to work together in co-operation both on
theoretical and applied aspects.
Since September 2000 seven COLLNET conferences were
organized in six countries. The 8th COLLNET Meeting will be held
in March 2007 in New Delhi, India.
Introduction
The increase in scientific-technical collaboration in the course of
history has been vividly documented through a number of
analytical studies.
For example, it has been shown that between 1650 and 1800 not
more than 2.2% of scientific papers were published in coauthorship.
By contrast, the second half of the 20th century is
characterized the world over by teamwork and coauthorships in the natural sciences and in medicine, i.e.
about 60-70% of the scientific papers were published during
this period in co-authorship. (DeB. Beaver & Rosen 1978;
1979a & b).
With the importance of collaboration in research and technology
growing world-wide, it has become necessary to examine the
processes involved in order to become aware of the implications
for the future organization of research as well as those for science
and technology policy. This has led to an increase in the number
of scientific studies of this topic internationally. (Glanzel 2002,
Borgman, C.L. & Furner, J. 2002).
The outstanding works of Donald deB. Beaver (1978), Derek John
de Solla Price (1963) and others on the topic of collaboration in
science have, over a number of years, encouraged a number of
scientists working in the field of quantitative scientific research to
concentrate their research in this field.
This has led both to an increase in the number of relevant
publications concerning this topic in international magazines, and
to an increase in the number of lectures in international
conferences (Basu 2001, Braun et. al. 2001, Davis 2001,
Havemann 2001, Wagner-Döbler 2001, Kundra & Tomov 2001).
By all accounts, this field of research is required to be a
comprehensive and diversified area ranging from small-group
research in social psychology/sociology to large network analyses
conducted into international co-authorship or citation networks,
including the concomitant observation of informal communication
via interviews or interrogative surveys on bibliometrical analyses.
A common bibliometric method for measuring the cooperation is
the analysis of co-authorship networks. A suitable webometric
method has to be developed in the future.
There are various references to the positive effect of "multiauthored papers" in the co-authorship network: for example
several studies show that international cooperation is linked with a
higher `citation impact' (Glänzel 2002).
The investigation of these processes can be made by analyses at
the micro level (individuals), at the meso level (institutions) or at
the macro level (countries) (Glänzel 2002).
In the field of science studies one most frequently comes across
investigations on international cooperation in science, followed by
cooperation relationships between institutions.
The last few years have seen an ascendancy in how to treat these
international issues. However, this trend has still failed to provide
a concept on a fundamental and interrelated theory regarding the
theme entitled ´Collaboration in science and in technology´. The
different approaches taken so far have revealed the shortcomings
of integration.
On account of the diversity of these issues it is possible to obtain
promising results only against the backdrop of an interdisciplinary
approach and from an intercultural viewpoint.
Both aspects are of basic importance in COLLNET.
In summary:
The rise in collaboration in science and technology experienced
world-wide at national and international level, has assumed such
an overriding importance that there is now an urgent need
perceptible to study such processes with a view to acquiring
fundamental knowledge for organizing future research and its
application to science and technology policies.
Foundation of COLLNET
Therefore in the year 2000 the time had come in the meantime to
create a global interdisciplinary research network COLLNET on
the topic "Collaboration in Science and in Technology" with
64 members
from 20 countries of all continents.
The members intended to work in cooperation on both theoretical
and applied aspects on the topic "Collaboration in Science and in
Technology".
The focus of this group is to examine the phenomena of
collaboration in science, its effect on productivity, innovation and
quality, and the benefits and outcomes accruing to individuals,
institutions and nations of collaborative work and co-authorship in
science.
Web site:
www.collnet.de
Journal:
Journal of Information Management and Scientometrics
(Incorporating the COLLNET Journal)
COLLNET Meetings (2000-2006):
- First COLLNET Meeting, September 2000, Berlin, Germany
- Second COLLNET Meeting, February 2001, New Delhi, India
- Third COLLNET Meeting, July 2001, Sydney, Australia
- Fourth COLLNET Meeting, August 2003, Beijing, China
- Fifth COLLNET Meeting, March 2004, Roorkee, India
- Sixth COLLNET Meeting, July 2005, Stockholm, Sweden
- Seventh COLLNET Meeting, May 2006, Nancy, France
Papers in Co-authorship between COLLNET Members:
223 co-authored papers (lifetime, starting before official
foundation of COLLNET)
The establishment of COLLNET has been reported in a special
issue of the international journal Scientometrics. In this report, the
work of both the first and second meetings were outlined
(Kretschmer, H., L. Liang and R. Kundra, 2001). The history and
subsequent development of COLLNET is described in the
following sections.
The areas of expertise represented by member scientists in
COLLNET are varied: mathematics, physics, chemistry, biology,
medicine, history of science, social sciences and psychology. The
team includes many senior scientists such as directors and/or
deputy directors of large establishments, organizers and/or deputy
organizers of world conferences in the field of scientometrics and
informetrics as well as winners of the Derek John de Solla Price
Medal.
Among these are board members of the International Society for
Scientometrics and Informetrics (ISSI), members of the German
Society for Psychology and advisors to the international journal,
Scientometrics.
Current principal investigators, mainly from the field of quantitative
scientific research (scientometrics and informetrics), engage in
teamwork on the nature, characteristics, growth and policy
relevance of collaboration and co-author networks.
It is proposed to include in future more experts from other fields of
scientific research and particularly from the social sciences, such
as psychology and sociology.
COLLNET has been an important catalyst for research on
collaboration and has provided opportunities for members to meet
face to face at various international conferences such as at ISSI
conferences (held every two years since 1987).
However, neither of these international conferences is focussed
solely on issues relating to collaboration or collaborative networks,
thus establishment of COLLNET in 2000 has opened an important
forum in which ideas and work on these issues is exchanged.
Closer personal contact between members inevitably leads to
formal and informal agreements on collaborative projects on these
crucial issues in research production.
Growth of Collaboration/Communication
Structures in COLLNET
Since 2000
Two studies are presented:
-
Development of informal and formal contacts between
COLLNET members
-
Development of the co-authorship network
Development of Informal and Formal Contacts
Between COLLNET Members
The questionnaire distributed to all of the COLLNET members
asked for the following details:
Names of those COLLNET members with whom
informal (loose) contacts exist in some form (either as
e-mail or exchange of reprints).
-
Names of those with whom formal (intensive) contacts
exist in the form of discussions on common projects
with definitive titles or in the form of co-authorship of
joint papers.
The development of collaborative growth within the framework of
COLLNET has been illustrated in Figures 2, 3 and 4.
Fig. 2 shows the number of informal (loose) contacts among the
COLLNET-members at the time of the Second COLLNET Meeting
in February 2001.
All the COLLNET members are compiled
country-wise. 16 countries participated in
COLLNET in the month of February. The line
joining the front corner of Fig.2: (1/1) to the
opposite rear corner (16/16) represents the
main diagonal in which the contacts among
COLLNET members of the same country
have been plotted. As seen from Fig. 2,
February 2001 witnessed the maximum
number of informal (loose) contacts among
COLLNET members within Germany (1/1)
and between Germany and India (1/2).
Informal contacts between other countries
can also be observed.
Fig. 3 shows the number of the formal (intensive) contacts (joint
projects or papers with definitive titles) as on the date of
establishment of COLLNET, viz. 1st January 2000.
Fig. 4 shows the increase in these formal contacts over the one
and a half years preceding the 3rd COLLNET Meeting.
Fig. 2
Fig. 3
Fig. 4
It can be seen from the main diagonal in Fig. 3 that at the time
when COLLNET was established, almost all the formal (intensive)
contacts existed only among members belonging to the same
country of origin.
However, Fig. 4 shows that during the subsequent period, the
intensive contacts had expanded across the different countries.
Fig. 4 resembles Fig. 2 in the graphical structural representation
of informal (loose) contact.
Social Network Analysis (SNA) of COLLNET
Sample Set
The bibliographies data of the 64 COLLNET members were
examined, under them:
-
26 female and 38 male scientists
30 members from the European Union (EU) and 34 from
non-European Union countries (N)
From the 34 members from the non-European Union countries (N)
we have :
-
3 from Australia
7 from America (4 of them from North America)
19 from Asia
4 from Eastern Europe
1 from South Africa
The last COLLNET data are from June 2003.
Data
Assuming that the reflection of collaboration is not limited to
articles in SCI- or other data bases,
a request was made to all the 64 COLLNET members for
their complete bibliographies, independently of the type
of the publications and independently from the date of
appearance of these publications.
From these bibliographies all publications were selected that
appeared
in co-authorship between at least two COLLNET
members.
Thus, it concerns
223 bibliographic multi-authored publications.
From this, the respective number of common publications
between two members was determined as the basis for the
analysis of the co-authorship network (SNA).
The co-authorship network developed according to this method
covers the entire lifetime collaboration between the COLLNET
members.
Developmental and structural formation processes in the
bibliographic networks are studied.
For information and brief overview the classification of the 223
bibliographic multi-authored publications according to their type is
shown:
CATEGORIES
1. Articles in Scientometrics
2. Articles in JASIS
13
3. Papers in monographs
68
4. Papers from conference proceedings 77
5. Books
NUMBER
55
Total Sum
223
10
Methods (SNA)
Otte and Rousseau (2002) recently showed that social network
analysis (SNA) can be used successfully in the information
sciences, as well as in studies of collaboration in science.
The authors showed interesting results by the way of an example
of the co-authorship network of those scientists who work in the
area of social network analysis.
Otte and Rousseau refer in their paper to the variety of the
application possibilities of SNA, as well as to the applicability of
SNA to the analysis of social networks in the Internet
(webometrics, cybermetrics).
Introduction to SNA
(copied partly from the paper by Otte and Rousseau, 2002)
Network studies are a topic that has gained increasing
importance in recent years. The fact that the Internet is one large
network is not foreign to this. Social network theory directly
influences the way researchers nowadays think and formulate
ideas on the Web and other network structures such as those
shown in enterprise interactions. Even within the field of
sociology or social psychology network studies are
becoming increasingly important.
In their article Otte and Rousseau are going to study social
network analysis and show how this topic may be linked to the
information sciences. It goes without saying that also Internet
studies are to be mentioned, as the WWW represents a social
network of a scale unprecedented in history.
Interest in networks, and in particular in social network analysis,
has only recently bloomed in sociology and in social
psychology.
There are, however, many related disciplines where networks play
an important role. Examples are computer science and artificial
intelligence (neural networks), recent theories concerning the
Web and free market economy, geography and transport
networks.
In informetrics researchers study citation networks, cocitation networks, collaboration structures and other forms of
social interaction networks.
What is social network analysis?
(copied partly from the paper by Otte and Rousseau, 2002)
Social network analysis (SNA), sometimes also referred to as
‘structural analysis’, is not a formal theory, but rather a broad
strategy of methods for investigating social structures.
The traditional individualistic social theory and data analysis
considers individual actors making choices without taking the
behaviour of others into consideration.
This traditional individualistic approach ignores the social
context of the actor. One could say that properties of actors are
the prime concern here.
In SNA, however, the relations between actors become the
first priority, and individual properties are only secondary.
Social network analysis conceptualises social structure as a
network with ties connecting members and focuses on the
characteristics of ties rather than on the characteristics of the
individual members.
One distinguishes two main forms of SNA: the ego-network
analysis, and the global network analysis. In ‘ego’ studies the
network of one person is analysed. An example in the information
sciences is White’s description of the research network centred on
Eugene Garfield. In global network analyses one tries to find all
relations between the participants in the network.
Growth in the number of published articles in the field of SNA
The Fig. below clearly shows the fast growth of the field in recent
years. More specifically, the real growth began around 1981, and
there is no sign of decline.
160
140
Number of articles
120
100
80
60
40
20
0
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year of publication
articles in Sociological Abstracts
articles in Medline Advanced
articles in PsycINFO
Growth of social network analysis by Otte and Rousseau
Some notions from graph theory
(copied partly from papers by Otte and Rousseau, 2002):
Directed and undirected graphs:
A directed graph G, in short: digraph, consists of a set of nodes,
denoted as N(G), and a set of links (also called arcs or edges),
denoted as L(G). In this text the words ‘network’ and ‘graph’ are
synonymous.
In sociological research nodes are often referred to as ‘actors’. A
link e, is an ordered pair (X,Y) representing a connection from
node X to node Y.
Node X is called the initial node of link e, X = init(e), and node Y is
called the final node of the link: Y = fin(e). If the direction of a link
is not important, or equivalently, if existence of a link between
nodes X and Y necessarily implies the existence of a link from Y
to X we say that this network is an undirected graph.
A path from node X to node Y is a sequence of distinct links (X,
u1), (u1,u2), … , (uk,Y).
A
B
C
D
The length of this path is the number of links.
The length of the path from A to D can be 1 or 2 or 3.
In this article we only use undirected graphs. Consequently, the
following definitions are only formulated for that case.
A co-authorship network is an example of an undirected graph:
if author A co-authored an article with author B, automatically
author B co-authored an article with A. An undirected graph can
be represented by a symmetric matrix M = (mXY), where mXY is
equal to 1 if there is an edge between nodes X and Y, and mXY is
zero if there is no direct link between nodes X and Y.
A
B
C
D
A
B
C
D
Symmetric matrix M = (mXY)
A
B
C
D
A
1
B
1
C
1
D
1
Asymmetric matrix M = (mXY)
A
B
C
D
A
B
C
1
D
1
Components:
A component of a graph is a subset with the characteristic that
there is a path between any node and any other one of this
subset. If the whole graph forms one component it is said to be
totally connected.
A
B
C
D
E
F
There are 2 components above.
Next we define some indicators describing the structure
(cohesion) of networks and the role played by particular
nodes.
Many more are described in the literature, but we will restrict
ourselves to these elementary ones.
Cliques
A clique in a graph is a subgraph in which any node is directly
connected to any other node of the subgraph Example:
A
B
C
D
Indicators
The density of a co-authorship network (D) is an indicator for
the level of connectedness of this network:
D = Number L of edges divided by the maximum number Lmax of
edges in the network.
It is a relative measure with values between 0 and 1.
Lmax=V (V-1)/2 with V=number of nodes
D = 2L / V(V-1)
A
B
C
D
D = 2*2 / 4*3=0.33
In addition, we shall also focus on some selected indicators of
centrality describing the structure of networks and the role played
by particular nodes (In analogy to Otte and Rousseau 2002,
Wassermann & Faust 1994), Centrality measures:
*Degree Centrality
*Closeness
*Betweenness
•Degree Centrality of a node A is equal to the number of nodes
(or edges) that are adjacent to A:
DCA=EA
A
B
C
D
DCA=3
The Degree Centrality of a node A is equal to the number of
his/her collaborators or co-authors. An actor (node) with a high
degree centrality is active in collaboration. He/she has
collaborated with many scientists.
The Degree Centrality in a V-node network can be standardised
by dividing by V-1:
DCAs=DCA/(V-1)
Example above: DCAs=3/3=1
Mean Degree Centrality (MDC) of the network is the ratio of the
sum of the Degree Centralities of all the nodes in the network to
the total number of nodes:
MDC=2L/V
Example above: MDC=2*3/4=1.5
Closeness Centrality of a node is equal to the total distance (in
the graph) of this node from all other nodes.
CA= ΣYdAY
where dAY is the number of ties in a shortest path from node A to
node Y.
A
B
C
D
The length of the path from A to D can be 1 or 2 or 3.
dAD=1; dAC=1; dAB=1
CA=3
Closeness is an inverse measure of centrality in that a larger
value indicates a more central actor. For this reason the
standardised closenenss is defined as
CAs= (V-1)/ CA
making it again a direct measure of centrality. CAs= (4-1)/ 3=1
The Closeness Centrality can be calculated only in
connected graphs or in connected subgraphs because the
shortest path between two nodes of disconnected graphs is
infinite (∞), for example the shortest path between B and E .
A
B
C
D
E
F
Betweenness Centrality BCA is the number of shortest paths
(distance dxy) that pass through A.
Otte and Rousseau mention actors (nodes) with a high
betweenness play the role of connecting different groups or
are ´middlemen´.
Wasserman and Faust (1994, p. 188) mention: ´Interactions
between two nonadjacent actors might depend on the other actors
in the set of actors who lie on the paths between the two.
These “other” actors potentially might have some control over the
interactions between the two nonadjacent actors.´ A particular
“other” actor in the middle, the one between the others, has
some control over paths in the network.
BCA=ΣX,Y GXAY/ GXY
GXAY is the number of shortest paths from node X to node Y
passing through node A.
GXY is the number of shortest paths from node X to node Y
(X,Y≠A).
A
B
C
D
shortest path from node B to node C: dBC=1
B to C: GBC=1; (not passing through node A), GBAC=0; GBAC/ GBC=0
B to D: dBD=2; GBD=2; GBAD=1;
GBAD/ GBD=1/2=0.5
C to D: dCD=1; GCD=1; GCAD=0;
GCAD/ GCD=0
BCA=0.5
It can be shown that for an V-node network the maximum
value for BCA is (V²-3V+2)/2. Hence the standardised
betweenness centrality is:
BCAs= 2* BCA/(V²-3V+2)
In the example above:
BCAs= 2*0.5/(42-3*4+2)=1/6=0.17
Example:
BCU=ΣX,Y GXUY/ GXY
BCU (a)=6
BCU (b)=4
BCUs (a)=1
BCUs (b)=0.67
BCU (c)=4
BCUs (c)=0.67
The general formula:
CNETWORK=(ΣX (Cmax-CX))/max value possible
can be applied for determining degree, closeness or
betweenness centrality for the whole network. These measures
are relative measures with values between 0 and 1.
Example: Group Degree Centralization
Σ vi=1 (DCmax - DCX)
GDC= -----------------------(V-1)(V-2)
The DCX in the numerator are the V Degree Centralities of the
nodes and DCmax is the largest observed value.
This index reaches its maximum value of 1 when one actor
(node) has collaborated with all other V-1 actor, and the other
actors interact only with this one, central actor. This is exactly the
case in a star graph. The index attains its minimum value of 0
when all degrees are equal
A
C
E
B
D
Σ vi=1 (DCmax - DCX)
GDC= -------------------------(V-1)(V-2)
DCmax= DCE=4
DCX= DCA= DCB= DCC = DCD=1
DCmax – DCX=4-1=3
GDC=3*4/(5-1)(5-2)=1
Example: An SNA co-authorship network
(partly copied from the paper by Otte and Rousseau, 2002):
In this section Otte and Rousseau perform a network analysis of
authors in the field of social network analysis.
We will point out the central players and the underlying
collaborative relationships between authors.
Co-authorship, a (strong) form of collaboration, is not the only way
to describe relations between scientific authors. Citation network,
for instance, could reveal other relations, but these are not studied
in this article.
In the 1601 articles dealing with SNA there were 133 authors
occurring three times or more.
Forming an undirected co-authorship graph (of these 133 authors)
led to a big connected component of 57 authors, 2 components
of 4 authors, 2 components of 3 authors, 7 small components
consisting of two authors and 48 singletons.
We will further concentrate on the central cluster of 57 authors.
Most important scientists in the field belong to this cluster.
Network analysis was performed using UCInet while the map was
drawn with Pajek (Package for Large Network Analysis).
The Fig. below shows the network of network analysts (central
cluster of 57 authors).
The network of network analysts by Otte and
Rousseau
Legend
1. D.D. Brewer
2. E.J. Bienenstock
3. S.D. Berkowitz
4. M. Gulia
5. P. Bonacich
6. H.R. Bernard
7. V. Batagelj
8. K. Carley
9. K.E. Campbell
10. P. Doreian
11. J.S. Erger
12. L.C. Freeman
13. K. Faust
14. A. Ferligoj
15. N.E. Friedkin
16. T.J. Fararo
17. J. Galaskiewicz
18. J.S. Hurlbert
19. C. Haythornthwaite
20. V.A. Haines
21. N.P. Hummon
22. I. Jansson
23. E.C. Johnsen
24. D. Krackhardt
25. P.D. Killworth
26. M.J. Lovaglia
27. B.A. Lee
28. P.V. Marsden
29. B. Markovsky
30. M.S. Mizruchi
31. D.L. Morgan
32. C. McCarthy
33. M. Oliver
34. S. Potter
35. B. Potts
36. T. Patton
37. D. Ruan
38. J. Skvoretz
39. J.W. Salaff
40. T.A.B. Snijders
41. J.J. Suitor
42. F.N. Stokman
43. G.A. Shelley
44. M. Spreen
45. J. Szmatka
46. S.R. Thye
47. M.A.J.Van Duijn
48. G.G. Van de Bunt
49. B. Wellman
50. C. Webster
51. S. Wasserman
52. D. Willer
53. E.P.H. Zeggelink
54. K.L. Woodard
55. S.L. Wong
56. N.S. Wortley
57. S. Robinson
The density for the central network of network analysts is 0.05.
So this network is clearly not dense at all, but very loose.
The author with the highest degree centrality is Barry Wellman
(University of Toronto), who has a degree centrality of 9. The
degree-centrality of the whole network is 11%, indicating that
many authors are not connected.
Another way of studying centrality is using the closeness
indicator. This indicator is more general than the previous one,
because it takes the structural position of actors in the whole
network into account. A high closeness for an actor means that
he or she is related to all others through a small number of
paths.
The most central author in this sense is Patrick Doreian
(University of Pittsburgh). The closeness of the whole network is
14%.
Betweenness is based on the number of shortest paths passing
through an actor. Actors with a high betweenness play the role of
connecting different groups, are ‘middlemen’ and so on. Again
Patrick Doreian has the highest betweenness. The betweenness
of the whole network is 47%.
UCInet found 16 cliques, this means: 16 subgraphs consisting
of three or more nodes. The largest one consists of 6 authors:
Bernard, Johnsen, Killworth, McCarty, Shelley and Robinson.
The second largest one consists of the five authors: Erger,
Lovaglia, Markovsky, Skvoretz and Willer.
Bibliometric analysis
The most prolific authors in SNA (highest number of papers)
show also a central role in the SNA network.
Results: Collaboration Networks in COLLNET
(partly copied from the paper by Kretschmer, H. & Aguillo, I.)
In analogy to the study of the network of the network analysts by
Otte and Rousseau this paper examined the COLLNET
collaboration network.
Additionally, the development of the bibliographic COLLNET
co-authorship network is examined over a specific time
period. Thus, the social network analysis (SNA) is applied to
structure formation processes in bibliographic networks.
The results of the Web network (Reflection of the bibliographic
network in the Web) are presented in a separate paper as well
as Gender studies in the network.
First let us have a view at the collaboration network obtained from
the bibliographies in 2003 including all of the life time papers.
1. Isidro Aguillo
2. Petra Ahrweiler
3. R. Ambuja
4. Elise Bassecoulard
5. Aparna Basu
6. Donald deB. Beaver
7. Sujit Bhattacharya
8. Maria Bordons
9. Martina Brandt
10. Mari Davis
11. Leo C.J. Egghe
12. Isabel Gomez
13. Ulla Grosse
14. Brij Mohan Gupta
15. Frank Hartmann
16. Frank Havemann
17. William W. Hood
18. Margriet Jansz
19. Karisiddappa
20. Sylvan Katz
21. Ved Prakash Kharbanda
22. Hildrun Kretschmer
23. Ramesh Kundra
24. Loet Leydesdorff
25. Liming Liang
26. Sofía Liberman
27. Zeyuan Liu
28. Valentina Markusova
29. Martin Meyer
30. Yoshiko Okubo
31. Farideh Osareh
32. Koti S. Raghavan
33. Ravichandra Rao
34. Ronald Rousseau
35. Jane Russell
36. Shivappa Sangam
37. Andrea Scharnhorst
38. Annedore Schulze
39. Dimiter Tomov
40. Rainer Voss
41. Caroline Wagner
42. Roland Wagner-Döbler
43. Yan Wang
44. Vera Wenzel
45. Concepcion S. Wilson
46. Paul Wouters
47. Yishan Wu
48. Michel Zitt
49.-64. are singletons up to June 2003. These 16 singletons are not included in the figure.
Bibliographic Co-authorship Network
The methods of social network analysis (SNA) are related to
Wassermann & Faust (1994) and to Otte & Rousseau (2002).
There are 64 "nodes" (= 64 COLLNET members) in the
network above (network from 2003)
48 of these COLLNET members (= 75%) have published in
co-authorship at least once with at least one of the other
COLLNET members. That means, at least 1"edge" is
adjacent to each of these 48 "nodes".
Differently expressed: Between two COLLNET members A
and B, there exists an edge if both have published at least
one publication in co-authorship. The members A and B
are called "pair of collaborators” (A,B).
There are LB=63 edges between the nodes or in other
words 63 different pairs of collaborators respectively.
-
-
-
A path from node X to node Y is a sequence of distinct
edges between pairs of collaborators:
(X, A1), (A1, A2), …, (Aj, Y)
The length of the path is equal to the number of distinct
edges. The shortest path from X to Y is called distance
dXY.
The co-authorship structure of COLLNET is a
"disconnected graph", i.e., there is not a ''path'' between
each pair of nodes X and Y. However the COLLNET
members can be divided into several "connected subsets".
A path also exists between all pairs of nodes in a
"connected subset". The "connected subsets" are denoted
as "components'' or ''cluster".
-
However between a pair of nodes from different components
there exists no path.
The COLLNET co-authorship network consists of 23
components:
*1 large central component of 32 members (57 by Otte and Rousseau)
*1 component of 4 members (2 by O. & R.)
*2 components of 3 members (2 by O. & R.)
*3 components of 2 members (7 by O. & R.)
*16 singletons (48 by O. & R.)
The largest cluster covers 50% of the COLLNET members (43% in
the network by Otte and Rousseau). In addition there are 22 small
and very small (singletons) clusters (59 by O. & R.).
This structure of clusters, which contain a single very large
cluster and also a large number of small clusters, is in agreement
with the existing findings in the literature (Newman 2001, Genest &
Thibault 2001, Kretschmer 2003, Otte & Rosseau 2002). It is
possible this could denote a general rule in a special type of coauthorship network (?).
The studied bibliographic co-authorship network in 2003 is a
network with low density of DB=0.031 (similar to the network of
network analysts, studied by Otte and Rousseau: D=0.05).
However because of intended development studies the
COLLNET results refer to the whole network but the results by
O.& R. to the largest component only. Therefore, maybe the
density value by O. & R. is higher than the other.
The indicators density, mean degree centrality and betweenness
centrality are applied in analyses of the bibliographic coauthorship network.
The general formula is applied for Betweenness.
Furthermore, the development of number of edges, number
of components, number of singletons and the size of largest
component (number of nodes in the largest component) are
studied (Table 2).
Development of COLLNET
Four stages are considered in the development of COLLNET:
•Until 1997: Collaboration of the future COLLNET members
before 1998 (preliminary stage)
•Until 1999: Collaboration until 1999 (cumulative,
including collaboration until 1997, i.e. preliminary
stage and first step of COLLNET development)
• Until 2001: Collaboration until 2001 (cumulative,
including collaboration until 1997, i.e. preliminary
stage, first and second steps of COLLNET
development)
• Until 2003: Collaboration until 2003 (cumulative,
including collaboration until 1997, i.e. preliminary
stage, first, second and third steps of COLLNET
development)
Collaboration until 1997
Collaboration until 1999
Collaboration until 2001
Collaboration until 2003.
Table 2: Development of Bibliographic Networks
1997
1999
2001
2003
Number of
edges or of
pairs of
collaborators
16
25
47
63
Number of
components
48
44
30
23
Number of
singletons
39
36
22
16
Size of largest 7
component
11
23
32
Density
.008
.012
.023
.031
Mean degree
centrality of
the network
MDC
.53
.78
1.47
1.97
Betweenness
.008
.028
.101
.22
The values of the indicators describing the structure of networks
(density, mean degree centrality and betweenness) increase from
1997 to 2003 with a particular rise from 1999 to 2001 (cf. Figure).
The growth in the number of pairs of collaborators (edges) is in
correspondence with the growth of density.
The probability is high that both the foundation of COLLNET and
first COLLNET meeting in 2000 maybe the reasons for this
increase.
•Structure Formation Process Measured by Entropies
•Whereas the size of the largest component grows, the number of
components and the number of singletons diminish (cf. Table 2).
This kind of structure formation processes in both the
bibliographic and the Web networks can be measured by
entropies H:
•There is a series of numbers Kf(f=1,2,…z), Kf ≠0
•
z
•h f =Kf / Σ Kf
•
f=1
•
z
•H = - Σ hf · lg2hf
•
f=1
•Kf is the size of a component f. The number of components in the
network is called z.
•The structure formation process is characterized by the growth of
the number of edges (pairs of collaborators), the decreasing
number of clusters, the growth of the large cluster and the
decreasing number of singletons (Table 2).
•The entropy H is decreasing with increasing size of the
components and with decreasing number of components.
•The maximum entropy H is reached in a network under the
condition there are singletons only. The minimum entropy is
reached under the condition where there is one large cluster only
and there are not any other components.
•The structure formation processes in the bibliographic network is
shown in the figure above.
Some Details of the Development of COLLNET
Networks
First step of the development of COLLNET (1998-1999):
An important trigger to the creation of COLLNET was the first
Berlin Workshop on Scientometrics and Informetrics/Collaboration
in Science, Berlin, August 1998.
This workshop was organized by the Association of Science
Studies (Gesellschaft fuer Wissenschaftsforschung e.V., Berlin),
and supported by the Free University Berlin, and DFG.
Second step (2000-2001):
Two years later in September 2000, in conjunction with the
Second Berlin Workshop on Scientometrics and
Informetrics/Collaboration in Science and in Technology, the first
COLLNET meeting was held at the Free University Berlin. From
this time on, COLLNET meetings have been regularly held
regularly: the Second COLLNET Meeting at the National Institute
of Science, Technology and Development Studies (NISTADS) in
February 2001 in New Delhi (India). Again, COLLNET used the
synergy of conjoint activity with the “International Workshop on
Emerging Trends in Science and in Technology Indicators:
Aspects of Collaboration”.
A third COLLNET Meeting took place in July 2001 in Sydney
(Australia) in conjunction with the 8th International Conference on
Scientometrics and Informetrics.
Third step (2002-2003):
Future strategies were discussed at the 4th COLLNET Meeting
which took place on Agust 29th, 2003, in Beijing in conjucntion
with the 9th ISSI Conference (ISSI - International Society for
Scientometrics and Informetrics). At this time, further measures of
the effectiveness of collaborative engagements among members
and productivity in the field of ‘collaboration in science and in
technology’ were discussed.
Thus, these 3 steps, along with the additional inclusion of the
preliminary stage, will be incorporated to show the development of
the bibliographic COLLNET co-authorship network in 4 stages:
Four stages derived from the 3 steps:
•Until 1997: Collaboration of the future COLLNET members
before 1998 (preliminary stage)
•Until 1999: Collaboration until 1999 (cumulative, including
collaboration until 1997, i.e. preliminary
stage and first
step of COLLNET development)
• Until 2001: Collaboration until 2001 (cumulative, including
collaboration until 1997, i.e. preliminary
stage, first and
second steps of COLLNET
development)
• Until 2003: Collaboration until 2003 (cumulative, including
collaboration until 1997, i.e. preliminary
stage, first,
second and third steps of COLLNET development)
References
Balaban, A. T. & Klein, D. J. (2002). Co-authorship, rational Erdös numbers,
and resistance distances in graphs, Scientometrics, 55, 59-70
Basu, A. & R. Aggarwal (2001). International collaboration in science in India
and its impact on international performance, Scientometrics, 52, 379-394
Batagelj, V., Ferligoj, A., and Doreian, P. (1992). Direct and indirect methods
for structural equivalence, Social Networks, 14, 63-90
Beaver, D. deB. & Rosen, R. (1978). Studies in Scientific Collaboration. Part
III. Professionalization and the Natural History of Modern Scientific CoAuthorship. Scientometrics, 3, 231-245
Borgman, C. L., & Furner, J. (2002). Scholarly communication and
bibliometrics. In B. Cronin (Ed.), Annual review of information science and
technology: Vol. 36 (pp. 3-72). Medford, NJ: Information Today.
Braun, T., Glänzel, W. & Schubert, A. (2001). Publication and cooperation
patterns of the authors of neuroscience journals. Scientometrics, 51, 499510
Davis, M. & C.S. Wilson (2002), Elite researchers in ophthalmology: Aspects
of publishing strategies, collaboration and multi-disciplinarity. Scientometrics,
52, 395-410
Glänzel, W. (2002).Coauthorship patterns and trends in the sciences (19801998): A bibliometric study with implications for database indexing and
search strategies. Library Trends, 50, 461-473
Genest, C. & Thibault, C. (2001). Investigating the concentration within a
research community using joint publications and co-authorship via
intermediaries. Scientometrics, 51, 429-440
Havemann, F. (2001): Collaboration behaviour of Berlin life science
researchers in the last two decades of the twentieth century as reflected in
the Science Citation Index, Scientometrics, 52, 435-444
Herring, S. C. (2002). Computer-Mediated Communication on the Internet.
In: Cronin, B. (ed.), Annual Review of Information Science and Technology
36, Medford, NJ: Information Today Inc., pp. 109-168.
Ingwersen, P. (1998). The calculation of Web Impact Factors. Journal of
Documentation, 54(2), 236-243.
Kling, R. & McKim, G. (2000). Not Just a Matter of Time: Field Differences in
the Shaping of Electronic Media in Supporting Scientific Communication.
Journal of the American Society for Information Science, 51(14), 1306-1320.
Kretschmer, H., L. Liang & R. Kundra (2001); Foundation of a global
interdisciplinary research network (COLLNET) with Berlin as the virtual
center, Scientometrics, 52, 531-538
Kretschmer, H. & M. Thelwall (2004): From Librametry to Webometrics.
Journal of Information Management and Scientometrics. Vol. 1, No. 1,
(2004), 1-7
Kretschmer.H. (2004). Author productivity and Erdös distances in coauthorship and in Web networks. Scientometrics. Vol.60, No.3, 409-420
Kundra, R. & D. Tomov (2001), Collaboration patterns in Indian and
Bulgarian epidemiology of neoplasms in Medline for 1966-1999
Newman, M. (2001). The structure of scientific collaboration networks. Proc.
Natl. Sci. USA, 98, 404-409
Otte, E. & Rousseau, R. (2002). Social network analysis: a powerful strategy,
also for the information sciences. Journal of Information Science, 28, 443455
Price, D.J. de Solla. (1963). Little Science, Big Science. New York: Columbia
University Press. (dt.1974. Little Science, Big Science. Von der Studierstube
zur Großforschung. Frankfurt am Main: Suhrkamp Verlag
Schubert, A. (2002)The Web of Scientometrics. A statistical overview of the
first 50 volumes of the journal. Scientometrics, 53, 3-20
Terveen, L.G and Hill, W.C. Evaluating Emergent Collaboration on the Web, in
Proceedings of CSCW 1998 (Seattle WA, November 1998), ACM Press, 355362.
Thelwall, M. (2003). What is the link doing here? Beginning a fine-grained
process of identifying reasons for academic hyperlink creation. Information
Research, 8,
Vaughan, L. and Shaw, D. (2003) Bibliographic and Web Citations: What Is
the Difference? Journal of the American Society for Information Science and
Technology, 54(14), 1313-1322.
Wagner-Dobler, R. (2001), Continuity and discontinuity of collaboration
behaviour since 1800- from a bibliometric point of view, Scientometrics, 52,
503-518
Wasserman, S. & Faust, K. (1994). Social network analysis. Methods and
applications. Cambridge: Cambridge University Press 1994
Wilkinson, D., Harries, G., Thelwall, M. & Price, L. (2003). Motivation for
academic web site interlinking: evidence for the web as a novel source of
information on informal scholarly communication. Journal of Information
Science, 29, 59-66
Thank You!