=Paper=
{{Paper
|id=Vol-2037/paper5
|storemode=property
|title=A Social Network Analysis Based Approach to Deriving Knowledge About Research Scenarios in a Set of Countries
|pdfUrl=https://ceur-ws.org/Vol-2037/paper_5.pdf
|volume=Vol-2037
|authors=Paolo Lo Giudice,Paolo Russo,Domenico Ursino
|dblpUrl=https://dblp.org/rec/conf/sebd/GiudiceRU17
}}
==A Social Network Analysis Based Approach to Deriving Knowledge About Research Scenarios in a Set of Countries==
https://ceur-ws.org/Vol-2037/paper_5.pdf
A Social Network Analysis based approach to
deriving knowledge about research scenarios in a
set of countries⋆
Paolo Lo Giudice1 , Paolo Russo2 , and Domenico Ursino3
1
DIIES, University “Mediterranea” of Reggio Calabria
2
NEGG
3
DICEAM, University “Mediterranea” of Reggio Calabria
(DISCUSSION PAPER)
Abstract. In this paper, we propose a new Social Network Analysis
based approach to providing a multi-dimensional picture of the research
scenarios of a set of countries of interest and to detecting possible “re-
search hubs” operating therein. This knowledge allows innovation man-
agers to understand the impact of different socio-economic conditions on
the research level of a country. Furthermore, it may help the design of
policies for supporting the accumulation of scientific and technological
capabilities. In the last part of this paper, we apply our approach to four
North African countries (i.e., Algeria, Egypt, Morocco and Tunisia) in
such a way as to show its potential.
1 Introduction
In the last years, scientometrics and bibliometrics received a growing interest
both in research literature and as objective ways for evaluating the performances
of researchers, universities, institutions, etc. [12, 10, 9, 7, 3]. The obvious conse-
quence of this fact is that more and more innovative approaches to support-
ing these activities have been recently proposed. Social Network Analysis [13]
and, more in general, graph theory, represent a prominent family of approaches
adopted in the past in this context (see, for instance, [4, 5, 8, 6, 2]). Furthermore,
it is possible to foresee that they will be even more adopted in the future.
This paper aims at providing a contribution in this setting. Indeed, it pro-
poses a new Social Network Analysis based approach to extracting knowledge
about research scenarios and spillovers [11] or, better, hubs in a set of countries.
As for this paper, a hub is a research institution that operates as a guide or
stimulus to research in its country and, at the same time, is capable of stim-
ulating cooperations with institutions of other countries. Our hub definition is
fitted to the scenario of our interest and strongly benefits from the observations,
suggestions and experience of innovation management researchers, who guided
⋆
The authors thank Proff. Fabio Landini and Roberto Mavilia of the Invernizzi Center
for Research on Innovation, Organization, Strategy and Entrepreneurship of Univer-
sity “Bocconi”, who supplied data for experiments and cooperated in the analysis
of results.
us in its formulation. Our approach is general and can be directly applied to
any set of countries. The only requirement is to have at disposal the set of the
publications of all the research institutions of the countries to investigate. In
this paper, we applied it to four North African countries (e.g., Algeria, Egypt,
Morocco and Tunisia), and we exploited all the publications of all the research
institutions of the four countries of interest in the time interval [2003, 2013], as
stored in the Web of Science repository [1]. The most important support data
structure is a social network whose nodes represent institutions and whose edges
denote collaborations among institutions. Starting from it, other important sup-
port data structures and accompanying parameters (some of which were never
defined in the literature) are introduced.
This paper is organized as follows. In Section 2, we illustrate our approach.
In Section 3, we apply it to the four North African countries mentioned above.
Finally, in Section 4, we draw our conclusions and overview some possible future
developments.
2 Description of our approach
Our approach operates on a set P ub of publications at our disposal and on a set
C of countries to investigate. It will be described in the next subsections.
2.1 Hub characterization and detection
In this section, we aim at defining a method for detecting both hubs and their
features in a set of countries. For this purpose, we preliminarily introduce a first
support data structure. It is a social network: G = ⟨N, E⟩. N is the set of the
nodes of G. A node ni ∈ N corresponds to exactly one institution registered in
our database. Since there is a biunivocal correspondence between a node of N
and the corresponding institution, in the following, we will use the symbol ni to
indicate both of them. Each node of N is labeled with an element of C depending
on the country of the corresponding institution. We indicate by li the label of
ni . E is the set of the edges of G. There exists an edge eij = (ni , nj , wij ) ∈ E if
there exists at least one publication involving one author of ni and one author of
nj . wij is the weight of eij ; it denotes the number of publications having at least
one researcher of ni and one researcher of nj among their authors. If a paper
has more than one author of the same institution ni , then there is a self-edge
linking ni with itself.
Now, we are able to introduce the concept of hub. With regard to this fact,
we point out that we do not aim at proposing a new concept, characterized by
a mathematical foundation supporting it. Instead, we would like to introduce
an informal and empirical, yet reasonable, concept, which can support innova-
tion managers to make their decisions. In carrying out this activity, we strongly
benefited from the observations, suggestions and experience of innovation man-
agement researchers, who guided us in its formulation. Taking this purpose into
account, we can say that, in order to be a hub, an institution must satisfy the
following conditions: (i) C1 : it should have published a very high number of pa-
pers; (ii) C2 : it should have published a high number of papers in cooperation
with institutions different from the ones of its country; (iii) C3 : it should have
published many papers in cooperation with institutions of its country.
To “quantify” conditions C1 , C2 and C3 , we use three metrics, namely M1 , M2
and M3 , respectively. M1 coincides with the classical weighted degree centrality,
M2 coincides with the normalized weighted degree centrality and M3 is analogous
to the E-I index [13].
As theoretically conjectured in the past literature, and as verified for the
countries composing our case study, M1 , M2 and M3 follow a power law distri-
bution. Taking all these considerations into account, the set HX of hubs for the
countries into consideration can be defined as the set of those institutions simul-
taneously belonging to the top X% of the institutions with the highest values
of M1 , M2 and M3 (we call I1X , I2X and I3X these three sets, when considered
separately). In this definition, X is a threshold allowing the selection of the in-
stitutions having the highest values of M1 , M2 and M3 . The choice to use X as
a threshold parameter derives from the power law distributions characterizing
all the three metrics. Reasonable values of X could be 10, 15 and 20. After sev-
eral experiments, we decided to consider a default value of X equal to 20. As a
consequence, in the following, when X is not specified, we intend that it is equal
to 20. Below, we use the symbol HkX to indicate the hubs of a given country k.
2.2 Investigation of the research scenarios
In this section, we aim at analyzing the research scenarios of the countries into
examination. Initially, we can introduce three indicators that could give us some
knowledge about the research scenarios of the countries into consideration. The
first one, RQ, is an indicator of the overall research quality in the countries
of interest. In fact, it measures how many institutions of I1 belong to these
countries. The second one, F C, indicates how many institutions, among the top
ones of the countries of interest, publish many papers with foreign institutions.
The third one, T P , indicates how many institutions that publish very much with
foreign institutions belong to the top institutions of the countries of interest.
In the investigation of the research scenario of a country k and of the role of
its hubs, it appears very interesting to analyze its paper distribution. For this
purpose, we introduce the average number AvgP ubH k of the publications of its
hubs. Another interesting issue to investigate is to verify if a hub of k publishes
more with institutions of k (we call “internal” the corresponding publications)
than with foreign ones (we call “external” the corresponding publications) or
alone. To carry out this investigation, we introduce: (i) the average number
AvgHubP ubIk of publications performed by the hubs of k with institutions of k;
(ii) the average number AvgHubP ubF k of publications performed by the hubs of k
with foreign institutions; (iii) the average number AvgHubP ubA k of publications
performed alone by the hubs of k (we call them “alone publications” in the
following).
A further interesting analysis is devoted to understand if, in their cooperation
with foreign institutions, the hubs of a given country k privilege one or few
countries. For this purpose, we specialize to our research context the Herfindahl
Index. This index is very used in economics. It is defined as the sum of the
squares of the market shares of the firms within the industry, where market
shares are expressed as fractions. It can range from 0.0 to 1.0, moving from a
huge number of very small firms to a single monopolistic producer. In our case,
we extend the Herfindahl index to our context and define the Herfindahl Index
HIk associated with the papers published by the hubs of k to verify if these hubs
published in cooperation with institutions of few (implying high values of HIk )
or many (implying low values of HIk ) countries.
2.3 Cooperation among hubs of the same country
In this section, we aim at investigating the cooperation levels of the hubs of
a given country k. For this purpose, we preliminarily define a support data
structure called clique social network. In particular, let G be the social network
defined in Section 2.1 and let Gk be its “projection” on the country k. Let Ck
be the set of cliques of Gk and let Hk be the set of the hubs of k. A clique social
network CGk has a node for each hub of Hk belonging to at least one clique of
Ck . Each node ni of CGk has associated a weight wi denoting the number of
cliques of Ck which it belongs to. An edge (ni , nj ) of CGk denotes that ni and
nj together belong to at least one clique of Ck .
Some measures capable of quantitatively representing the differences that
characterize the cooperation among hubs are the following: (i) the number of
cliques |Ck |; (ii) the absolute dimension dCk of the largest clique in Ck ; (iii) the
d
relative dimension |HCkk| of the largest clique in Ck ; (iv) the fraction fCHk of hubs
belonging to at least one clique of Ck . In order to avoid that results are biased by
the number of publications (which can be very different in the different countries
of interest), we defined a normalized version CG dk of CGk . Finally, we searched
for some measures to compare clique social networks. After several experiments,
we found that the most significant ones were: (i) the number of nodes; (ii) the
number of edges; (iii) density4 .
2.4 Investigation about the quality of publications
All indicators introduced above are based only on the number of publications.
Actually, it would be important to take also their quality into account. One way
to do this consists in taking their impact factor into consideration; another way
consists in considering the number of citations received by papers. Impact factors
are measured only for journal papers. As a consequence, if we want to exploit
this measure, we must define a new support data structure. This structure, that
we indicate by G′ , is, once again, a social network. It is defined as G′ = ⟨N ′ , E ′ ⟩.
4
Actually, this last measure can be derived from the two other ones. However, it is
very expressing and, consequently, we decided to explicitly consider it.
There is a node ni ∈ N ′ for each institution having at least one author, who
published at least one journal paper. An edge e′ij = (n′i , n∑
′ ′
j , wij ) has a seman-
′
tics similar to the one of eij except that the weight wij = p∈(P ubij ∩JP ub) IFp
considers both the number of publications performed by ni and nj simultane-
ously and the corresponding impact factors. Paper citations are valid both for
conference proceedings and for journal papers. However, in order to make our
analyses about the quality of publications homogeneous, we chose to investigate
only journal papers. In this case, we used the same support social network as
′
the one∑exploited for impact factors, but the edge weights wij was computed as:
′
wij = p∈(P ubij ∩JP ub) CitNp , where CitNp is the number of citations of p.
2.5 Characterization of hub neighborhoods
A first parameter useful to characterize hub neighbors is the average number
AvgP ub of publications of the hub neighborhoods. A second parameter regards
their average dimension AvgDim. Even in this case, we disaggregate data per
country, and we call AvgDimk the corresponding parameter for the country k.
A next analysis regards the cooperation level among the institutions belong-
ing to hub neighborhoods. To perform this task, we define a new support social
network. We call it nbh social network and we represent it by means of the
symbol N bhGi . Given a neighborhood nbhi , the corresponding nbh social net-
work is defined as follows: nbhGi = ⟨nbhi , nbhEi ⟩. There is a node in N bhGi for
each node of nbhi ; there is an edge (ni , nj ) ∈ nbhEi if there exists at least one
publication between an author of ni and an author of nj .
After having introduced this social network, we define a first parameter on it.
This parameter is called AvgCF rac and corresponds to the average fraction of
the real number of cliques existing in hub neighborhoods against their possible
number. It is an indicator of the cooperation level among hubs. As usual, we
call AvgCF rack the “projection” of AvgCF rac on the country k. A second
parameter about intra-neighborhood cooperation regards the average fraction
AvgCN bh of the number of cliques existing in hub neighborhoods against the
number of neighborhood nodes. Again, we call AvgCN bhk the “projection” of
AvgCN bh on the country k. A final parameter measuring the cooperation level
among hub neighbors is the average density AvgDens of the nbh social network.
As usual, we call AvgDensk the “projection” of AvgDens on the country k.
3 Application of our approach to four North African
countries
As pointed out in the introduction, we applied our approach to four North
African countries, namely Algeria, Egypt, Morocco and Tunisia. As a conse-
quence, in our case study, the set C of the countries to investigate (see Section
2) consisted of the following elements: C = {‘A’, ‘E’, ‘M’, ‘T’, ‘O’}, where ‘A’
(resp., ‘E’, ‘M’, ‘T’, ‘O’) stands for ‘Algeria’ (resp., ‘Egypt’, ‘Morocco’, ‘Tunisia’,
‘Others’). Clearly, ‘O’ indicates all the countries different from the four into
Fig. 1. Number of hubs for each country Fig. 2. Herfindahl index over time for the
in the year interval [2003,2013] four countries
examination. We chose these four countries because we had the possibility to
access the corresponding data thanks to an academic partner that was carrying
out a project aiming at investigating their research and innovation scenarios.
Our dataset was stored in a MongoDB database. To give an idea of it, we report
some of its features: (i) dimension = 10.27 GB; (ii) number of institutions =
278,696 ; (iii) number of authorships = 89,008,846; (iv) number of publications
= 6,599,104; (v) number of research areas = 6; (vi) number of research fields =
251. Due to space limitations we can present only a very limited number of the
results that we obtained.
In Figure 1, we report the variation of the number of hubs for each country.
From the analysis of this figure, we can see that the country with the highest
number of hubs is Tunisia. This result was unexpected also because both the
extension and the number of citizens of Tunisia were smaller than the ones of
the other three countries.
In Figure 2, we report the Herfindahl index HIk for the four countries. From
the analysis of this figure we can observe that Tunisia and Algeria have a high
Herfindahl index, which implies that their hubs cooperate mostly with one or few
countries. In particular, after some investigations, we have seen that Tunisia and
Algeria cooperate mostly with France, which is reasonable if we consider that
they are French past colonies. By contrast, Egypt has a very low Herfindahl
index, i.e., its hubs cooperate with many countries. An interesting trend is the
one of Morocco; in fact, it initially has a behavior like the ones of Tunisia and
Algeria, whereas, in the last years, it shows a behavior like the one of Egypt.
To determine the cooperation levels among hubs for the four North African
countries into consideration, for each country k, we performed the following
tasks: (i) we considered the two time intervals [2003, 2009] and [2007, 2013]; (ii)
we computed the clique social networks CG1k (resp., CG2k ), corresponding to
the first and the second time intervals, respectively; (iii) we measured the four
parameters introduced in Section 2.2 for quantitatively evaluating clique social
networks. Obtained results for the first time interval are reported in Table 1.
d1C
Country |C1k | d1C k f 1H
Ck
k |Hk |
Algeria 292 7 0.152 0.913
Egypt 38 13 0.351 0.973
Tunisia 130 8 0.116 0.942
Morocco 82 7 0.127 0.818
Table 1. Quantitative differences characterizing the cooperation behaviors of hubs in
the four countries of interest in the time interval [2003, 2009]
Fig. 3. Graphs CG2k for all the four countries
From the analysis of this table we can draw the following conclusions: (i) Egypt
has the largest clique; this clique is much larger than the maximum cliques of
the other countries; (ii) in Egypt almost all hubs belong to at least one clique.
These results indicate that Egyptian hubs are more prone to cooperation than
the hubs of the other countries.
In Figure 3, we report the graphs CG2k for all the four countries; in these
graphs the dimension of nodes is proportional to the corresponding weight, i.e.,
to the number of cliques they belong to. The analysis of this figure confirms the
previous conjecture; in fact, the number of edges in the Egyptian graph is much
higher than in the other graphs. This fact, along with the presence of many not
very large nodes, allows us to derive another important knowledge pattern, i.e.,
that research activities in Egypt are more “distributed” among hubs.
4 Conclusion
In this paper, we have proposed a new Social Network Analysis based approach
to investigating the research scenarios of a set of countries of interest and to de-
tecting possible hubs operating in these countries. Extracted knowledge allows
the evaluation of the impact of different socio-economic conditions on research
and favors the design of policies for supporting innovation in the countries of
interest. We applied our approach to four North African countries. In the fu-
ture, we plan to exploit techniques for analyzing information diffusion in social
networks to understand how the possible mobility of top researchers from one
institution to another can impact on the quality of both of them. Moreover, we
plan to investigate the possible application of classification techniques to derive
hub profiles in different countries. Furthermore, it is necessary to consider that
a network changes over time. As a consequence, hubs could also change over
time. The evolution of an institution’s capability of becoming hub, remaining
hub or no longer being a hub is a challenging task that we plan to investigate.
Finally, we plan to analyze the possible application of prediction techniques to
understand what kind of financial investment must be performed for maximizing
the increase of both the number and the quality of hubs and publications in the
countries of interest.
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