Advances on Social Network Applications
J.UCS Special Issue
Jason J. Jung
(Department of Computer Engineering
Yeungnam University, Gyeongsan, Korea
j2jung@intelligent.pe.kr)
Przemyslaw Kazienko
(Wroclaw University of Technology
Wroclaw, Poland
Przemyslaw.Kazienko@pwr.wroc.pl)
Social networks have been one of the important issues in various
domains, e.g., e-business [Jung 2012], and e-learning [Gan and Zhu
2007, [Jung 2010]. In our computer systems, we have possibility to
process data about interactions and activities of millions of
individuals. Communication and multiple user technologies allow us to
form large networks which in turn shape and catalyst our
activities. Due to scale, complexity and dynamics, these networks are
extremely difficult to analyze in terms of traditional social network
analysis methods [[Jung 2009a]. On the other hand, the data about human
communication, common activities and collaboration simultaneously
provide new opportunities for new applications [Jung 2008b, [Jung
2008a].
This special issue is devoted to analysis of these large-scale social structures and what is more important to the identification of the areas where social network analysis can be applied to provide the knowledge that is not accessible for other types of analysis. Additionally, applications of social networks analysis can be investigated either
from static or dynamic perspective. We seek for business and
industrial applications of social network analysis that help to solve
real-world problems. The area of social networks analysis and its
applications bring together researchers and practitioners from
different fields and the main goal of this special issue is to provide
for these people the opportunity to share their visions, research
achievements and solutions as well as to establish worldwide
cooperative research and development. At the same time, we want to
provide a platform for discussing research topics underlying the
concepts of social network analysis and its applications by inviting
members of different communities that share this common interest of
investigating social networks.
The first paper in this issue, authored by Qinna Wang and Eric Fleury,
proposes two methods (which are called clique optimization and fuzzy
detection) to detect overlapping community structure. Clique optimization detects
granular overlaps which are nodes have high togetherness with
different communities. Fuzzy detection identified modular overlaps
which are groups of nodes shared by several communities.
The second paper authored by John Boaz Lee et al. introduces an
interesting scheme to understand social dynamics in online
elections. The Wikipedia Request for Adminship (RfA) process has
studied within the context of a social network and several factors
influencing different stages of the voting process are
pinpointed. This work has found that voters tend to participate in
elections that their contacts have participated in.
In the third paper, Onur Can Sert et al. focus on multi-objective
optimization problem. They assume all potential solutions belong to
different experts and in overall and ensemble of solutions finally has
been utilized for finding the final natural clustering. They have
evaluated on categorical datasets and compared them against single
objective clustering result in terms of purity and distance measure of
k-modes clustering.
The fourth paper by JooYoung Lee et al. introduces two social network
based algorithms that automatically compute `author reputation' from a
collection of textual documents. Firstly, keyword reference behaviors
of the authors are extracted to construct a social network, which
represents relationships among the authors in terms of information
reference behavior. Then, by using the network, these two algorithms
are applied to compute each author's reputation value.
The fifth paper by I-Hsing Ting et al. proposes an efficient method to
understand the online users for assisting companies to enhance the
accuracy and efficiency of the target market. A social recommendation
system based on the data from microblogs is built according to the
messages and social structure of target users. The similarity of the
discovered features of users and products will then be calculated as
the essence of the recommendation engine.
The sixth paper by Anna Zygmunt et al. focuses on identifying key
persons, who are active in social groups in the blogosphere by
performing various social network analysis. Mainly, two approaches are
considered in the paper: (i) discovery of the most important
individuals in persistent social communities and (ii) regular
centrality measures applied either to social groups or the entire
network.
This special issue has been achieved by a number of fruitful
collaborations. We would like to thank the editor in chief of Journal
of Universal Computer Science (JUCS), Hermann Maurer, for his kind
support and help during the entire process of publication. This was
possible thanks to the work of the renowned researchers that provided
their anonymous reviews.
Finally, we are most grateful to the authors for their valuable
contributions and for their willingness and efforts to improve their
papers in accordance with the reviewers suggestions and comments.
References
[Gan and Zhu 2007] Gan, Y. and Zhu, Z.: A
learning framework for knowledge building and collective wisdom
advancement in virtual learning communities. Educational Technology
& Society, 10(1):206-226, 2007.
[Jung 2008a] Jung, J.J.: Ontology-based
context synchronization for ad-hoc social
collaborations. Knowledge-Based Systems, 21(7):573-580, 2008.
[Jung 2008b] Jung, J.J.: Query transformation
based on semantic centrality in semantic social network. Journal of
Universal Computer Science, 14(7):1031-1047, 2008.
[Jung 2009a] Jung, J.J.: Contextualized query
sampling to discover semantic resource descriptions on the
web. Information Processing & Management, 45(2):283-290, 2009.
[Jung 2010] Jung, J.J.: Integrating Social Networks for Context Fusion
in Mobile Service Platforms. Journal of Universal Computer Science,
16(15):2099-2110, 2010.
[Jung 2012] Jung, J.J.: Evolutionary Approach for Semantic-based Query
Sampling in Large-scale Information Sources. Information Sciences, 182(1):30-39, 2012.
|