=Paper=
{{Paper
|id=None
|storemode=property
|title=Affiliation Influence on Recommendation in Academic Social Networks
|pdfUrl=https://ceur-ws.org/Vol-866/poster5.pdf
|volume=Vol-866
|dblpUrl=https://dblp.org/rec/conf/amw/BrandaoM12
}}
==Affiliation Influence on Recommendation in Academic Social Networks==
https://ceur-ws.org/Vol-866/poster5.pdf
Affiliation Influence on Recommendation in
Academic Social Networks
Michele A. Brandão and Mirella M. Moro
Departamento de Ciência da Computação, Universidade Federal de Minas Gerais
micheleabrandao@dcc.ufmg.br, mirella@dcc.ufmg.br
Abstract. Social networks have been the focus of many studies, from
communities’ identification to link prediction. Here, we propose a method
based on researchers’ institution affiliation for predicting links in a col-
laboration social network. Initial experiments show that considering the
institution affiliation aspect, the set of recommendations is more accurate
and concise, leading to a more efficient result.
1 Introduction
A social network (SN) is a collection of individuals (or organizations) that have
relationships in a certain context, e.g. friendship and co-authorship [8]. Those
networks have been studied for over two decades in order to analyze the interac-
tions between people and detect patterns in such interactions [1]. Understanding
the mechanisms by which a SN evolves is a fundamental question that is still not
well solved [4]. Many methods have been proposed for different aspects of SN
analysis. Link prediction is one of such methods that may be applied in other
functions, such as those in recommender systems. In this work, we study the
recommendation problem in order to suggest new links in the network.
Specifically, we focus on a unique type of SN, the academic social networks,
which are formed by researchers and their connections (given by paper and
patent co-authorships, for example). In this research world, recommending new
links may help a researcher to form new groups or teams, to search for new
collaborations when writing a grant proposal and to investigate different research
communities. Moreover, a recent work shows that research groups with well
connected academic SN tend to be more prolific [6]. Discovering new links in
this scenario is not a trivial task, because the social proximity has different
interpretations in which the institutions, the connection between people and the
academic context (e.g. affiliation and research area) must be considered.
Related Work. SN analysis has become important for academic research com-
munities (such as mathematics [1]) to understand their own characteristics and
behavior [2]. Specifically, co-authorship networks are an important class of aca-
demic networks and have been analyzed in search for different characteristics
and behavioral patterns of scientific collaborations [5].
Given the researchers organized in a network, recommending people with
whom a researcher may collaborate is a way of predicting links. Regarding link
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prediction, in [4], the authors evaluate topological measures (e.g., Jaccard coef-
ficient) for classifying co-author collaborations. In [3], the author uses a topo-
logical measure to describe links occurrence. For instance, in [7], recommending
collaborations considers each co-author as a link in an academic SN.
Contributions. Given an academic social network (with researchers connected
to their co-authors) and the fact that well connected networks are more pro-
lific, this work aims to recommend collaborations by predicting links between
researchers. The novelty is: the recommendation function focuses on the re-
searchers affiliations by prioritizing persons from institutions with which the
researcher has already collaborated. The experimental evaluation shows that,
when compared to the state of the art method, our function does indeed return
more accurate and concise set of possible collaborators.
2 Recommending Collaborations
Overview. Social Networks are formed by actors and their relational ties [9].
The importance of a relationship between its actors may be defined by a weight
measure. In this paper, we use an academic social network, in which two re-
searchers are connected if they have co-authored a paper [9]. The final goal is to
recommend new collaborations over the academic network, which is mapped to
predicting links in a social network. We also explore how institution affiliations
affect the relationship between researchers. Specifically, we present a new rec-
ommendation (or prediction) function that identifies collaborations that may be
intensified as well as new collaborations that can be formed. The novelty relies
in the function that considers the researchers’ institution affiliation aspect.
Existing Recommendation Function. Different aspects of the SN may be
considered for defining a recommendation function. One of the most relevant
is the weight associated to the relationships. Each weight is important because
it reflects the link’s semantics, instead of just the network topological feature.
In other words, the weight semantics represents rich information from the SN
and their connections. Determining such weights is a great challenge and closely
related to the type of data the network models. For instance, in [7] (which also
uses a co-authorship network), the weight is defined by three different metrics:
cooperation (Cp), correlation (Cr) and social closeness (Sc). Cr and Sc are
combined to form an single metric Cr Sc (weighted average) with weights wCr
and wSc that define the importance of each one.
Affiliation Based Function. Here, we propose to consider the institution af-
filiation aspect in the relational ties’ weighting metrics. As byproduct, having
an institution-oriented weight provides more information to the SNA, such as
assisting in the search for collaborations with different institutions and analyz-
ing the influence of the cooperation with an institution upon the collaborations.
Hence, we introduce the affiliation index (Affin) that represents the new weight.
For any given pair of researchers hi, ji, Af f inij is defined by Equation 1.
N P Ii,j
Af f ini,j = (1)
N Ti
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where N P Ii,j is the number of papers of researcher i co-authored with people
from j’s institution, and N Ti is the total number of papers authored by i. Affin
follows the natural intuition that an institution is more important to an author,
if he has collaborated with someone from that institution, and hence is more
likely to contact other researcher in the same institution.
This way, we build upon the existing recommendation function from [7] by
adding Affin to it. Then, for each pair of researchers, the relationship among
Affin, Sc, Cp and Cr establishes the necessity (or not) of having more academic
interaction between them (in order to improve the overall connection of the
academic social network). We combine Affin and Sc to establish a single metric
Affin Sc defined by Equation 2.
wAf f in .Af f ini,j + wSc .Sci,j
Af f in Sci,j = (2)
wAf f in + wSc
where given an academic network with the authors i and j, Af f in Sci,j is a
weighted average, wAf f in and wSc weights determine, respectively, the impor-
tance of the metrics Affin and Sc to the resulting value. Hence, the weights may
be used for emphasizing either the affiliation or the social closeness.
In order to equally consider Af f in Sci,j , Cp and Cr indexes, we use degrees
to represent ranges of values that are possible: “high”, “medium” and “low”. The
actual values for the ranges may follow a linear scale (for example, low < 33%
and high > 66%). Equation 3 shows the combination of the indexes and their
recommended actions: “Initiate Collaboration” and “Intensify Collaboration”.
Initiate Collab, if (Cpi,j = 0)∧
(Af f in Sci,j > threshold);
ri,j = Intensif y Collab, if (Cpi,j ∈ low)∧ (3)
((Af f in ∈ medium) ∨ (Af f in ∈ high))∧
i,j i,j
((Cri,j ∈ medium) ∨ (Cri,j ∈ high));
where pairs of researchers with zero Cpi,j and nonzero Af f in Sci,j (we choose
“low” degree as threshold) are recommended to create a collaboration; and pairs
with “low” Cpi,j , “medium” or “high” Af f ini,j , and “medium” or “high” Cri,j
are recommended to intensify their collaborations.
Example. Figure 1 exemplifies the use of Equation 3. It presents five researchers
(from A to E) from different Brazilian institutions. For instance, considering the
pair of researchers hA, Bi, there is no recommendation because Af f in ScA,B <
“low” (moreover, Af f inA,B = 0 indicates that there has never been a collabo-
ration between the researchers’ institutions). On the other hand, hB, Ci is rec-
ommended to initiate collaboration, because CpB,C = 0 and Af f in ScB,C >
“low”. One observation when comparing Affin to previous work, the method in
[7] recommends to initiate collaboration in both cases.
Datasets. To evaluate Affin, we have applied the recommendation function to a
real social network (from CiênciaBrasil1 ), and compared its results to the state
of the art . Usually, precision is evaluated by actual users who verify whether
the recommendations make sense. Here, we take a different approach. In order
1
CienciaBrasil: http://pbct.inweb.org.br
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Fig. 1. Example of determining new collaborations within a partial Social Network
(a) Recall (b) Precision
Fig. 2. Effect of wSc variation on recommendation results
to have a fair comparison, we follow the evaluation given by [7] and consider the
network in two moments: until 2007 and until 2010. The baseline for measuring
precision and recall is given by the new collaborations that appeared between
2008 and 2010. By not considering the users’ opinion, as [7], we expect to have
small precision, giving more importance to the recall results.
Initial Results. Our method and [7] rely on the value of wSc . Hence, Figure 2
presents (a) Recall and (b) Precision obtained when varying the wSc values and
using a fixed wAf f in = 1 (and wCr = 1). These results show that the precision
and recall stabilize around wSc = 10. Then, the default value of weight wSc
chosen for the remainder evaluations is 10.
Table 1 shows the number of relevant recommendations retrieved (under-
lined), the total number of recommendations retrieved (in parentheses), preci-
sion and recall for Af f in and [7] in the recommendation of new collaboration.
The results show that varying the weight wAf f in and wCr (i.e, increasing the
importance of the metrics Affin and Cr to the resulting value), Affin is more
accurate and the number of relevant recommendations retrieved is greater than
the state of the art [7]. Furthermore, when increasing the importance of Affin,
the number of relationships recommended is reduced. Similarly, the results in
Table 2 show that Af f in is also more accurate than [7] in the recommendation
of intensify collaborations.
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Table 1. New collaborations (wSc = 10)
Weight Initiate
wAf f in /wCr Affin [7]
1 108 (9314) 108 (9545)
precision 1.16% 1.13%
Table 2. Intensify Collaborations
recall 81.82% 81.82%
5 107 (8653) 101 (9180) Relevant/ Preci-
precision 1.23% 1.10% Method Retrieved sion Recall
recall 81.06% 76.52% Affin 94 (223) 42.15% 71.21%
10 105 (7117) 97 (8415) [7] 80 (196) 40.82% 60.60%
precision 1.48% 1.15%
recall 79.54% 73.48%
15 97 (6131) 86 (7720)
precision 1.58% 1.11%
recall 73.48% 65.15%
3 Conclusion
This paper introduced a new function for recommending collaborations in an
academic social network. Its novelty relies on considering the institution affilia-
tion aspect (given by a metric called Af f in) with cooperation, correlation and
social closeness metrics. Our experiments show the new function can reduce the
number of recommendations and is more accurate than the state of the art. We
are currently refining the recommendation function by evaluating other metrics
that can improve even further the results. We are also working on a more through
experimental analysis considering different networks.
Acknowledgments. This work was partially funded by CAPES, CNPq,
Fapemig and InWeb, Brazil.
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