=Paper=
{{Paper
|id=None
|storemode=property
|title=Cooperative Authorship Social Network
|pdfUrl=https://ceur-ws.org/Vol-619/paper1.pdf
|volume=Vol-619
|dblpUrl=https://dblp.org/rec/conf/amw/LopesMWO10
}}
==Cooperative Authorship Social Network==
https://ceur-ws.org/Vol-619/paper1.pdf
Cooperative Authorship Social Network
Giseli Rabello Lopes1 , Mirella M. Moro2 , Leandro Krug Wives1 , and
José Palazzo Moreira de Oliveira1?
1
Universidade Federal do Rio Grande do Sul - UFRGS
Porto Alegre, Brazil
{grlopes,wives,palazzo}@inf.ufrgs.br
2
Universidade Federal de Minas Gerais - UFMG
Belo Horizonte, Brazil
mirella@dcc.ufmg.br
Abstract. This paper introduces a set of challenges for developing a
dissemination service over a Web collaborative network. We define spe-
cific metrics for working on a co-authorship research social network. As
a case study, we build such a network using those metrics and compare
it to a manually built one. Specifically, once we build a collaborative net-
work and verify its quality, the overall effectiveness of the dissemination
services will also be improved.
Key words: Social Networks, Dissemination Systems.
1 Introduction
Web 2.0 is the second generation of communities and services characterized by
providing techniques for the personal publication, sharing, collaboration, and or-
ganization of information on the World Wide Web. In this perspective, not only
the technological and content aspects but also the social interactions and its re-
lational aspects must be considered. In this context, the web-based communities,
hosted services, and web applications emerged, including Social Networks.
The Social Network Analysis (SNA) is based on the assumption that the
relationship’s importance between interaction units is a central point to the
evaluation and analysis of social interaction. Some fundamental concepts used
on SNA include actors and relational ties [1]. Actors are social entities that have
social linkages modeled by the Social Network (SN). Actors are linked to other
actors by relational ties.
The increasing interest in researching in SNA was encouraged by the popu-
larization of online social networks, which are very interesting Web applications.
Another example of such concepts application is a co-authorship social network
representing a scientific collaboration network. In this network, actors represent
authors and relational ties represent the relationships between pairs of authors.
The presence of at least one co-authored paper between two authors determines a
?
This research is partially supported by CNPq (Brazil), and is part of the InWeb
research project.
2 G. R. Lopes, M. M. Moro, L. K. Wives and J. P. M. de Oliveira
relational tie between them. Some examples of data sources for the construction
of this kind of networks are DBLP, Google Scholar, CiteSeer, among others.
The relational tie between authors may help to identify long term collabora-
tions, common research interests, preferred conferences, research groups under
formation, among others. Furthermore, as the social ties evolve, new research
interests and new collaborations will be identified. Any person who wants to
keep updated about such an evolution can be notified of such novel aspects by
adding a dissemination service to the social network.
A dissemination service is formed by data producers and consumers. Specif-
ically, consumers subscribe to the service by defining a profile, which is usually
composed of different queries. As the producers inject the system with new
data, usually through messages, the dissemination service evaluates each mes-
sage against the profiles. Once there is a match between a profile and a message,
the service sends that message to the profile’s consumer [2].
The contributions of this paper are twofold. First, we introduce a set of chal-
lenges for developing a dissemination service over a Web collaborative network.
Then, we tackle the challenges from the SN perspective. Specifically, we present
an architecture for such a dissemination service over a collaborative network.
The architecture is formed by different layers, from the Web to digital libraries,
social network, and the dissemination service. Based on the architecture, we were
able to identify research challenges that are innovative to the SN area. We define
specific metrics for working on a co-authorship research SN. Then, we build a
network using those metrics and compare it to a manually built one. Specifically,
once we build a collaborative network and verify its quality, the effectiveness of
the dissemination services will also be improved. Therefore, based on such an
evaluation, the dissemination service can identify (and recommend) the more
pertinent publications as well as identify possible hidden collaboration nets.
The paper is organized as follows. Section 2 describes the general context
of dissemination services and defines the base architecture. Section 3 introduces
the metrics to determine the weights of relational ties of a co-authorship Social
Network. Section 4 presents a case study that shows the construction of collab-
oration Social Network. It also evaluates the metrics employed to analyze the
SN. Section 5 presents some related work. Section 6 concludes this paper.
2 Dissemination Service in Social Network Context
Content-based dissemination is a form of data delivery that differs from tra-
ditional communications since the messages are delivered according to their
content rather than the IP address of their destination. There is a continu-
ous stream of messages from data producers to consumers, without any of the
human parties having knowledge of the other [2, 3]. This form of communication
is widely employed by dissemination services, which may be employed within
publish/subscribe systems (pub/sub for short).
In order to clarify how a dissemination service can work on a Web collabora-
tive network, we present a case study based on the academic field. It exemplifies
Cooperative Authorship Social Network 3
a service that disseminates new publications and research connections. Specifi-
cally, individuals (or organizations) can subscribe to research topics or researcher
names, for example. Once a new publication or a new collaboration is detected,
this information is disseminated to those subscribers whose keywords match such
new data. It is important to notice that not only publications are recommended
but also (and more important) new possible cooperations among researchers
are identified and suggested. The whole process is composed by six phases, as
illustrated in Figure 1. Each step of this process works as follows.
(1) The information about researchers is
mined from the Web or provided by indi-
viduals or organizations. Their actual pub-
lications or their curricula vitae are orga-
nized in semi-structured data. (2) A Digi-
tal Library (DL) stores and allows to man-
age such data. (3) A DL interactive pro-
cess feeds relevant information to build a
social-research network. (4) The dissemi-
nation service evaluates this huge volume
of connected data and identifies the result-
ing, filtered, qualified data. (5) This re-
sulting information is delivered to the in-
dividuals (researchers, students, profession-
als) and organizations (educational, govern-
mental, and industrial), and (6) published
Fig. 1. Dissemination service over Web col-back to the Web, providing universal access
laborative network and visibility to the research network data.
The dissemination service from Figure 1 illustrates tasks with challenges to
different Computer Science areas. Specifically, Information Retrieval techniques
may be employed along with Data Mining algorithms in order to recover the
researchers’ data from the Web (1). Moreover, Web Management issues become
critical when considering that the data will be extracted from the Web (for ex-
ample privacy, security, provenance, and credibility). The Digital Library main-
tenance presents new challenges due to the interactive nature of the framework
(2), where individuals and organization will access the data through the dissem-
ination service, and not through the Digital Library interface as usual. Social
Network’s mechanisms are necessary for defining the collaborative network (3).
Then, the challenges appear on the Dissemination service level, which also in-
clude Network Management (4). Finally, the actual dissemination and evaluation
of data involve Document Management, Distributed Systems, Parallel Comput-
ing, Security and Networks as well (5, 6).
It is important to notice that each of those disciplines is complex by na-
ture. Instead of discussing each of such areas, the focus of this paper is on the
social networks challenges. Specifically, with the increasing interest in Social
Networks, the interaction of the parties (data producers and consumers) within
4 G. R. Lopes, M. M. Moro, L. K. Wives and J. P. M. de Oliveira
the dissemination service will soon conquer the spotlight. In social networks, it
is important to qualify and quantify how individuals (people and organizations)
are connected, how tightly (or loosely) they interact, and what their common
interests are. Due to the large volume of data involved and the high complexity
of those connections, the development of an automatic mechanism capable of
efficiently identifying and analyzing such interactions is imperative.
3 Social Scientific Networks Analysis
Social Networks are based on the assumption of the relationship’s importance
between interaction units. The weights of the relational ties in a social network
aim to measure the importance of the ties between actors. It is necessary to
establish approaches to automatically determine these weights based on infor-
mation available about the actor’s relationships.
In this paper, we employ a scientific collaboration network as base example.
We present approaches to determine two types of associations namely Collabora-
tion in Co-authorship and Collaboration in Research Areas. These associations
were chosen because they cover certain facets of the relational ties of the collab-
oration network. According to Newman [4], that studied scientific collaboration
networks in which two scientists are considered connected if they have coau-
thored a paper, this seems a reasonable definition of scientific acquaintance.
3.1 Collaboration-based association - Co-authorship (Ca)
Formally, a Social Network SN of a co-author relationship a is a pair: SNa =
(N, E) where N and E are the set of N odes and Edges. Each edge e ∈ E is a
tuple of the form hai , t, w, aj i, where the edge is directed from ai to aj , t denotes
the type of association between ai and aj , and w denotes the weight affected
to the association. This weight is a numerical value between 0 and 1. In our
approach, the equation 1 determines the Collaboration in Co-authorship weight.
|aj co authorship|
wtCa(ai →aj ) = (1)
|ai author|
where:
– wtCa(ai →aj ) corresponds to the weight of the recommendation based on the
co-author relationship. The weight is different according to the relation di-
rection (the weight in the direction ai → aj is different than in aj → ai );
– |aj co authorship| corresponds to the number of times that the author aj
was a co-author of a paper with author ai ;
– |ai author| corresponds to the total number of papers of the author ai .
In other words, the higher this weight is, the more relevant is the relationship
with author aj to the author ai . The use of Ca metric implies that there is a
graph with 0 or 2 links between two authors. The weights represent the degree of
collaboration in co-authorship between the authors. This metric is an asymmetric
variant of the Jaccard Coefficient and it was applied in the context of Social
Networks by other works as [5, 6].
Cooperative Authorship Social Network 5
3.2 Collaboration-based association - Research Areas (Ra)
In this case, we consider the same definition of Social Network SN of co-author
relationship (as defined in the previous section). However, each edge e ∈ E is
a tuple of the form hai , t, r, w, aj i, where the edge is directed from ai to aj , t
denotes the type of association between ai and aj , r denotes the research area
associated to the relationship represented, and w denotes the weight affected to
the association. This weight is a numerical value between 0 and 1. The equation
2 provides the Collaboration in Research Areas weight.
Crresearch areas(ai ,aj ) co authorshipresearch area rx (ai ,aj )
wtRa(ai →aj ) = × (2)
|research areasai | co authorshipresearch areas(ai ,aj )
where:
– wtRa(ai →aj ) corresponds to the weight of the recommendation based on the co-
author relationship according to research areas. Again, the weight is different
according to the relation direction;
– Crresearch areas(ai ,aj ) corresponds to the number of research areas in which
the authors ai and aj published co-authored papers;
– |research areasai | corresponds to the total number of research areas in
which author ai published;
– co authorshipresearch area rx (ai ,aj ) corresponds to the number of co-author
relationship between authors ai and aj in the x area;
– co authorshipresearch areas(ai ,aj ) is the total number of co-author relation-
ship between authors ai and aj in every research areas in which they pub-
lished together.
The use of Ra metric implies that there are 2n links between two authors, be-
ing that n indicates the number of research areas in which the authors published
together. Each link has a direction, a research area and a weight associated. The
higher this weight is, the more relevant is the relationship with author aj to
the author ai in the research area x. In such an approach, we have the idea of
collaboration in research areas.
4 Case Study
This paper proposes an approach to construct a social network for collaborative
research. The complete work is under development as research project of the In-
Web (MCT/CNPq Grant Number 573871/2008-6), the Brazilian National Insti-
tute of Science and Technology for the Web. In fact, we have built a collaborative
social network based on the publications of the researchers associated to INWeb.
The Institute is formed by 27 researchers and their students. All researchers are
professors in a major education institution (namely UFMG, UFRGS, UFAM,
and CEFET-MG) with graduate program in Computer Science.
6 G. R. Lopes, M. M. Moro, L. K. Wives and J. P. M. de Oliveira
4.1 Building the Social Network: Manually and Automatically
Initially, this group of researchers was manually analyzed by a specialist. The
resulting network can be visualized in Figure 2. This network is used as baseline.
Rede Co-Autoria: UFMG + UFAM, UFRGS, CEFET
UFMG UFAM
Altigran S. da Silva
Clodoveu Edleno S. de Moura
Mirella A. Davis
Berthier A. M. Moro
Ribeiro-Neto João M. B.
Cavalcanti
Marcos
A. Gonçalves Alberto UFRGS
Laender Viviane M. Orengo
Raquel
O. Prates Carlos A. Heuser
Jussara Renata M. Galante
M. Almeida Nivio
Arnaldo Ziviani José Palazzo M. de Oliveira
A. Araújo
Leandro K. Wives
Gisele
L. Pappa
Virgílio A. CEFET/MG
F. Almeida Evandrino G. Barros
Renato
Ferreira Fabiano Botelho
Dorgival Cristina Murta
G. Neto Wagner Adriano
Genaína Nunes Rodrigues Meira Jr. C. M. Pereira
Fig. 2. Manual INWeb Social Network
For validating our metrics, we have implemented a tool to automatically
generate a Social Network. This SN was build using information about authors
provided by the DBLP digital library. It is important to notice that this library
is exported as an XML document. Instead of using the whole dataset, we ex-
tracted from the library just the papers written by the considered researchers and
published in conferences proceedings and in journals (as elements inproceedings
and article). Such a subset was chosen because this information is significantly
important for representing the co-author relationship between authors and, con-
sequently, to determine the research collaborations among them.
The actors of the SN can be chosen and they are a subset of authors with
scientific papers indexed by the DBLP. The relational ties between actors are the
relationships between pairs of authors. These social ties represent the co-author
relationships. The weights of the linkages are determined by equation 1. In that
equation, |ai author| corresponds to the total number of papers of the author ai ,
and it considers all papers to this author ai indexed at DBLP, including papers
that are not co-authored by authors in the SN who will be graphically presented.
The resultant INWeb Social Network constructed automatically is presented
in Figure 3. The data used in this case was collected from the DBLP repository
on January 21, 2009. This data gathering process summed up 677,345 authors;
692,431 conference proceedings papers and 432,663 journal articles.
After building them, we compared the two Social Networks: the manually
constructed SN (called Manual INWeb SN) and the automatically generated one
Cooperative Authorship Social Network 7
Legend:
1-Adriano M. Pereira
2-Alberto H. F. Laender
3-Altigran Soares da Silva
4-Arnaldo de Albuquerque Araújo
5-Berthier A. Ribeiro-Neto
6-Carlos A. Heuser
7-Clodoveu A. Davis
8-Cristina D. Murta
9-Dorgival Olavo Guedes Neto
10-Edleno Silva de Moura
11-Evandrino G. Barros
12-Fabiano C. Botelho
13-Genaı́na Nunes Rodrigues
14-Gisele L. Pappa
15-João M. B. Cavalcanti
16-José Palazzo Moreira de
Oliveira
17-Jussara M. Almeida
18-Leandro Krug Wives
19-Marcos André Gonalves
20-Mirella Moura Moro
21-Nivio Ziviani
22-Raquel Oliveira Prates
23-Renata de Matos Galante
24-Renato Ferreira
25-Virgı́lio A. F. Almeida
26-Viviane Moreira Orengo
27-Wagner Meira Jr.
Fig. 3. Automatic INWeb Social Network
(called Automatic INWeb SN). Comparing them against each other, we observed
that the Manual INWeb SN covers 93.44% of the Automatic INWeb SN. The
Automatic INWeb SN covers 83.82% of the Manual INWeb SN. Furthermore, if
we consider that the ideal network (144 edges) is the union between the edges of
the Manual INWeb SN (136 edges, considering that each linkage was reciprocal)
and the edges of the Automatic INWeb SN (122 edges), we have the following
results. The Manual INWeb SN recall is 94.44% and the Automatic INWeb SN
recall is 84.72%. The ideal network was considered the union because the Manual
INWeb SN was carefully developed by a specialist and the Automatic INWeb
SN was based on an occurrence of a co-authorship between two authors for the
establishment of the relational ties.
The main goal of this comparative analysis between the two networks was to
validate the Social Network constructed automatically by our system using the
DBLP dataset. The results obtained demonstrate that the DBLP digital library
is a good data source that considerably covers the co-authorship relations in
Computer Science, more specifically in Information Systems research area.
4.2 Analysis of the Automatic Co-authorship Network
In this section, we further analyze the Automatic INWeb Social Network. The
goal is to use other metrics to understand the properties of the Social Network on
this case study. In the next subsections, we present the metrics considered and
discuss the results obtained (observation: the results of the metrics were plotted
in decreasing order of the values obtained in all graphics and the authors were
represented by numbers in the range of 1 to 27 into accordance to the ascending
order of the full names (see Legend of Figure 3)).
Clustering Metrics. Clustering is a process that aims to identify subsets or
clusters of “similar” elements (or data items). The goal of clustering algorithms
8 G. R. Lopes, M. M. Moro, L. K. Wives and J. P. M. de Oliveira
is to create groups that are coherent internally, but clearly different from each
other. Thus, elements within a cluster should be as similar as possible; and
elements in one cluster should be as dissimilar as possible from elements in other
clusters [7]. In order to evaluate the clusters generated by those algorithms,
we can employ internal quality measures that require no human intervention,
such as cohesion and coupling [8]. Cohesion is the average pairwise similarity
of elements within the cluster. Coupling is the average pairwise similarity of
elements in which one element belongs to cluster C and the other does not.
The clustering metrics were adapted for evaluating our case study. We con-
sidered each group constituted by an author and all his co-authors as a cluster.
For each cluster (each author), we calculated the respective cluster metrics. The
similarity values for the metrics calculation are the weights of the relational ties
between authors. In our case, the best results will be that whose cohesion and
coupling measure high values. Such result is important because each cluster is a
subnet of the social network being analyzed.
The cohesion metric was adapted to consider two similarity values between
each pair of authors. This was necessary because our SN is represented by a
directional graph. The new equation is defined as follows (Equation 3).
m−1
X m−1
X
wt(ai →aj ) + wt(aj →ai )
i=1 j=i+1
cohesion(C) = (3)
m(m − 1)
where, m corresponds to the total number of authors in the group considered
(m=1(author)+n(total number of his/her INWeb co-authors)).
In this case, the similarity values used (wt ) in the calculation were the weights
wtCa . Figure 4 presents the cohesion results obtained to each cluster formed by
one author and all his INWeb co-authors. The results obtained show the average
of importance between all pairs of authors in each cluster considered. The more
cohesive groups are those formed by authors with high number of collaborations
whose weights indicate a high importance in these co-authorships.
As Figure 4 illustrates, some clusters formed by few authors have the best
results. This probably happened because these clusters are formed by young
authors whose importance weights in relation to their co-authors are high. Some
senior authors formed clusters with low cohesion values. This probably happened
because those worked with many co-authors over time and/or have a much larger
collaboration (cooperation) network that the one formed by INWeb authors.
Figure 5 presents the results for coupling metric. This graphic plots the au-
thors in x axis and the coupling values obtained for each cluster (formed by
the author and his co-authors) in y axis. Equation 4 was used. This metric was
evaluated by using the output weights to the author ai whose cluster C is being
analyzed as similarity value. Indeed, C is the cluster formed by an author and his
co-authors; m is the number of elements in the cluster C; and n is the number of
elements outside the cluster C belonging to a cluster Q formed by the co-authors
of ai and all co-authors of these co-authors of ai (including ai ). In this case, the
similarity values used in the calculation are the weights wtCa(ai →aj ) where ai was
Cooperative Authorship Social Network 9
0,600 0,300
0,500 0,250
0,400 0,200
Cohesion
Coupling
0,300 0,150
0,200 0,100
0,100 0,050
0,000 0,000
Authors Authors
Fig. 4. Cohesion results for INWeb Fig. 5. Coupling results for INWeb
the author been analyzed and aj varies among each author of the cluster Q.
X
sim(ci , qj )
i,j
coupling(C) = (4)
m×n
Note that the nonzero similarity values are between ai and his co-authors, and
between ai and ai himself. On the equation, the weight between the author and
himself was considered 1. This shows the coupling among the group of researchers
formed by each author and his co-authors. The results show that some young
researchers that have “good” publications present high coupling. This probably
occurred because such researchers work in more “condensed” groups while the
others have a larger network and/or work in several groups.
Complementary Analysis. This subsection presents other analysis per-
formed on the Automatic INWeb Social Network.
First, Figure 6 presents the percentage of INWeb Co-authors in relation of
the total Co-authors indexed by DBLP, for each author. This metric prioritizes
authors that have high number of his total co-authors within the INWeb Social
Network. The results show higher values to the authors that have his co-author
relationships represented more significantly by the INWeb partnerships.
35,00% 100
Total Number of publications
Percentage of Co-authors
30,00%
80
25,00%
20,00% 60
15,00% 40
10,00%
20
5,00%
0,00% 0
Authors Authors
Fig. 6. Percentage of Co-authors Fig. 7. Total number of publications
Figure 7 presents the total number of publications by author. This metric
is presented in order to help to understanding the results. The INWeb Social
Network shows that some authors do not have co-author relationship with any
INWeb author. However, Figure 7 shows that all authors of INWeb Social Net-
work have at least one publication indexed by DBLP.
Figure 8 shows the average importance of each author to his INWeb co-
authors. This metric was calculated according to the equation 5.
10 G. R. Lopes, M. M. Moro, L. K. Wives and J. P. M. de Oliveira
n
X n
X
wtaj →ai wtai →aj
j=1 j=1
In Avg Imp(ai ) = (5) Out Avg Imp(ai ) = (6)
n n
where ai corresponds to the author being analysed, aj varies among the co-
authors of ai , and n corresponds to the total number of co-authors of ai in the
Social Network being considered.
The graph in Figure 8 plots the authors in x axis and the input average
importance values obtained for each author in y axis. For calculating the impor-
tance (wtCa(ai →aj ) ), it considered the DBLP Social Network (i.e., all publications
indexed by DBLP were considered, whether they are co-authored by an INWeb
author or not). However, the co-authors considered were only those belonging
to the INWeb Network. Figure 8 also illustrates the relative importance of each
author to the others. The result shows that the equation prioritizes authors
who have a high average importance value to his co-authors. Some authors that
have few co-authors but have a meaningful importance value to his co-authors
overcame other authors that have a high number of co-authors.
0,350 1,000
Output Average Importance
Input Average Importance
(from author to INCT co-
0,300
(from INCT co-authors to
0,800
0,250
0,200 0,600
authors)
author)
0,150 0,400
0,100
0,050 0,200
0,000 0,000
Authors Authors
Fig. 8. Input Average Importance (fromFig. 9. Output Average Importance (from
author to INWeb co-authors) INWeb co-authors to author)
Figure 9 shows the average importance of all INWeb co-authors to each
author. This metric was calculated according to the equation 6. This graph plots
the authors in x axis and the output average importance values obtained for
each author in y axis. This graph illustrates the importance of the other INWeb
authors to each author in relation to all collaboration network represented by
DBLP SN. The result shows that authors that have a group of co-authors more
“condensed” and, sometimes, without interaction with other people outside of
this group, will often have higher values of output average importance.
5 Related Work
This section overviews some work related to recommender systems (a type of
dissemination system) and social networks.
Weng and Chang [9] propose a recommender method that employs ontologies
and the spreading activation model The ontologies are employed for defining
user profiles, being the basis to reason about the users’ interests. The spreading
activation model is used to search for other influential users in a Social Network
Cooperative Authorship Social Network 11
Golbeck et al. [10] present a website that integrates Social Networks on the
Semantic Web context and the trust concept for the generation of movies’ recom-
mendations. The Social Networks then indicate the trust ratings between users
by considering the path length between them.
Aleman-Meza et al. [5] define a solution for the Conflict of Interest (COI)
problem using Social Networks. The goal is to detect COI relationships among
authors of scientific papers and potential reviewers of these papers. Moreover,
rules are established to determine a possible degree of COI among the authors
based on the Social Networks built and the relationship’s weights between them.
Jeh et al. [11] propose a measure of structural-context similarity, called Sim-
Rank. The recommender systems were used as motivation. The base idea of the
model is that two objects are similar if they are related to similar objects.
Zaiane et al. [12] explore a Social Network coded within the DBLP database.
It considers a new random walk approach to reveal interesting knowledge about
the research community and even to recommend collaborations.
Menezes et al. [13] developed a geographical analysis of knowledge produc-
tion in Computer Science. They analyzed co-authorship Social Networks of the
Computer Science area.
Ganev et al. [14] developed a set of tools for building, exploring and querying
academic Social Networks. They proposed a measure reputation called visibility
as an adjusted PageRank applied on the Social Network context.
Our paper is related to all those since it focuses on solutions for Social Net-
works. However, we presented a case study to clarify how a dissemination service
can work on top of a Web collaborative network. We presented an approach to
construct a Social Network for collaborative research that considers new met-
rics. Our paper also adapts evaluation metrics to analyze the quality of the social
network obtained using the proposed approach.
6 Concluding Remarks
The section 4 analyzed the Automatic INWeb Social Network. In the future, we
plan to analyze the evolution of these results. We will also be able to compare
them against new analysis from other Social Networks. Regarding the dissem-
ination service, these results will also be useful. Specifically, once we build a
collaborative network and verify its quality (using the aforementioned metrics),
the quality of the dissemination services will also be improved. In other words,
the evaluation of the relational ties among the researchers (authors) ensures
better quality to the dissemination service. Therefore, based on such an evalua-
tion, the dissemination service can identify (and recommend) the more pertinent
publications as well as identify possible hidden collaboration nets.
As dissemination systems have recently grown from topic-based systems to
XML-enabled systems, we believe that the next step is for them to follow the data
technology and support any type of data uniformly (e.g. relational and XML).
Moreover, considering all the aspects involved from the other research areas,
we believe that the database technology must evolve to consider uniformly and
12 G. R. Lopes, M. M. Moro, L. K. Wives and J. P. M. de Oliveira
seamlessly any type of data there exist with extensible and Web-scalable features.
This complex scenario brings new and exciting issues to be handled by many
different Computer Science communities. Our final goal is to have a working
system that integrates our research groups. The results will be evaluated, at the
end of a four year period, by the access patterns and users evaluation of the
quality of the disseminated papers and, more important, by the increase in the
cooperation pattern among inter-institutional researchers. From the social point
of view, those features are the fundamental element to the integration to the
access of the content available at INWeb.
References
1. Wasserman, S., Faust, K.: Social Network Analysis: methods and applications.
Cambridge University Press (1994)
2. Diao, Y., Rizvi, S., Franklin, M.J.: Towards an internet-scale xml dissemination
service. In: VLDB. (2004) 612–623
3. Moro, M.M., Vagena, Z., Tsotras, V.J.: Recent Advances and Challenges in XML
Document Routing. In: Open and Novel Issues in XML Database Applications:
Future Directions and Advanced Technologies. IGI Global (2009) 136–150
4. Newman, M.E.J.: The structure and function of complex networks. SIAM Review
45 (2003) 167–256
5. Aleman-Meza, B., Nagarajan, M., Ding, L., Sheth, A.P., Arpinar, I.B., Joshi, A.,
Finin, T.W.: Scalable semantic analytics on social networks for addressing the
problem of conflict of interest detection. TWEB 2(1) (2008)
6. Mika, P.: Social networks and the semantic web. In: WI ’04, Washington, DC,
USA, IEEE Computer Society (2004) 285–291
7. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval.
Cambridge University Press (July 2008)
8. Kunz, T., Black, J.P.: Using automatic process clustering for design recovery and
distributed debugging. IEEE Trans. Softw. Eng. 21(6) (1995) 515–527
9. Weng, S.S., Chang, H.L.: Using ontology network analysis for research document
recommendation. Expert Syst. Appl. 34(3) (2008) 1857–1869
10. Golbeck, J., Hendler, J.: Filmtrust: movie recommendations using trust in web-
based social networks. In: IEEE CCNC - Consumer Communications and Net-
working Conference. Volume 1. (2006) 282–286
11. Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: ACM
SIGKDD. (2002) 538–543
12. Zaiane, O.R., Chen, J., Goebel, R.: Dbconnect: mining research community on
dblp data. In: WebKDD/SNA - Workshop on Web Mining and Social Network
Analysis. (2007) 74–81
13. Menezes, G.V., Ziviani, N., Laender, A.H., Almeida, V.: A geographical analysis
of knowledge production in computer science. In: WWW. (2009) 1041–1050
14. Ganev, V., Guo, Z., Serrano, D., Tansey, B., Barbosa, D., Stroulia, E.: An environ-
ment for building, exploring and querying academic social networks. In: MEDES
’09, New York, NY, USA, ACM (2009) 282–289