827,320 research outputs found
Static and Dynamic Aspects of Scientific Collaboration Networks
Collaboration networks arise when we map the connections between scientists
which are formed through joint publications. These networks thus display the
social structure of academia, and also allow conclusions about the structure of
scientific knowledge. Using the computer science publication database DBLP, we
compile relations between authors and publications as graphs and proceed with
examining and quantifying collaborative relations with graph-based methods. We
review standard properties of the network and rank authors and publications by
centrality. Additionally, we detect communities with modularity-based
clustering and compare the resulting clusters to a ground-truth based on
conferences and thus topical similarity. In a second part, we are the first to
combine DBLP network data with data from the Dagstuhl Seminars: We investigate
whether seminars of this kind, as social and academic events designed to
connect researchers, leave a visible track in the structure of the
collaboration network. Our results suggest that such single events are not
influential enough to change the network structure significantly. However, the
network structure seems to influence a participant's decision to accept or
decline an invitation.Comment: ASONAM 2012: IEEE/ACM International Conference on Advances in Social
Networks Analysis and Minin
Multislice Modularity Optimization in Community Detection and Image Segmentation
Because networks can be used to represent many complex systems, they have
attracted considerable attention in physics, computer science, sociology, and
many other disciplines. One of the most important areas of network science is
the algorithmic detection of cohesive groups (i.e., "communities") of nodes. In
this paper, we algorithmically detect communities in social networks and image
data by optimizing multislice modularity. A key advantage of modularity
optimization is that it does not require prior knowledge of the number or sizes
of communities, and it is capable of finding network partitions that are
composed of communities of different sizes. By optimizing multislice modularity
and subsequently calculating diagnostics on the resulting network partitions,
it is thereby possible to obtain information about network structure across
multiple system scales. We illustrate this method on data from both social
networks and images, and we find that optimization of multislice modularity
performs well on these two tasks without the need for extensive
problem-specific adaptation. However, improving the computational speed of this
method remains a challenging open problem.Comment: 3 pages, 2 figures, to appear in IEEE International Conference on
Data Mining PhD forum conference proceeding
THE IMPLICATIONS OF COMPUTERIZATION IN THE CHANGES OCCURRING IN THE ROMANIAN HIGHER EDUCATION VARIATION AND STRUCTURE
Nowadays, education represents a decisive factor for the general progress with deep impacts at spiritual, social and economics levels. The transition to a market economy in Romania calls for the necessity of knowledge and analysis of the structure and dynamics of higher education, widely known for its special contribution to society's development. The present paper tackles some aspects concerning the variance analysis of higher education network, as well as of the structural and dynamic modifications at its level, with reference to extending computer science process at this level of education. The conclusions obtained takes into consideration the causes and the objective changes which refer to the reform of the higher education and its future development.Computerization, Progress, Higher Educatin
An Introduction to Social Semantic Web Mining & Big Data Analytics for Political Attitudes and Mentalities Research
The social web has become a major repository of social and behavioral data that is of exceptional interest to the social science and humanities research community. Computer science has only recently developed various technologies and techniques that allow for harvesting, organizing and analyzing such data and provide knowledge and insights into the structure and behavior or people on-line. Some of these techniques include social web mining, conceptual and social network analysis and modeling, tag clouds, topic maps, folksonomies, complex network visualizations, modeling of processes on networks, agent based models of social network emergence, speech recognition, computer vision, natural language processing, opinion mining and sentiment analysis, recommender systems, user profiling and semantic wikis. All of these techniques are briefly introduced, example studies are given and ideas as well as possible directions in the field of political attitudes and mentalities are given. In the end challenges for future studies are discussed
A Geographical Analysis of Knowledge Production in Computer Science
We analyze knowledge production in Computer Science by means of coauthorship networks. For this, we consider 30 graduate programs of different regions of the world, being 8 programs in Brazil, 16 in North America (3 in Canada and 13 in the United States), and 6 in Europe (2 in France, 1 in Switzerland and 3 in the United Kingdom). We use a dataset that consists of 176,537 authors and 352,766 publication entries distributed among 2,176 publication venues. The results obtained for different metrics of collaboration social networks indicate the process of knowledge production has changed differently for each region. Research is increasingly done in teams across different fields of Computer Science. The size of the giant component indicates the existence of isolated collaboration groups in the European network, contrasting to the degree of connectivity found in the Brazilian and North-American counterparts. We also analyzed the temporal evolution of the social networks representing the three regions. The number of authors per paper experienced an increase in a time span of 12 years. We observe that the number of collaborations between authors grows faster than the number of authors, benefiting from the existing network structure. The temporal evolution shows differences between well-established fields, such as Databases and Computer Architecture, and emerging fields, like Bioinformatics and Geoinformatics. The patterns of collaboration analyzed in this paper contribute to an overall understanding of Computer Science research in different geographical regions that could not be achieved without the use of complex networks and a large publication database
Information dynamics algorithm for detecting communities in networks
The problem of community detection is relevant in many scientific
disciplines, from social science to statistical physics. Given the impact of
community detection in many areas, such as psychology and social sciences, we
have addressed the issue of modifying existing well performing algorithms by
incorporating elements of the domain application fields, i.e. domain-inspired.
We have focused on a psychology and social network - inspired approach which
may be useful for further strengthening the link between social network studies
and mathematics of community detection. Here we introduce a community-detection
algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method
by considering networks' nodes as agents capable to take decisions. In this
framework we have introduced a memory factor to mimic a typical human behavior
such as the oblivion effect. The method is based on information diffusion and
it includes a non-linear processing phase. We test our method on two classical
community benchmark and on computer generated networks with known community
structure. Our approach has three important features: the capacity of detecting
overlapping communities, the capability of identifying communities from an
individual point of view and the fine tuning the community detectability with
respect to prior knowledge of the data. Finally we discuss how to use a Shannon
entropy measure for parameter estimation in complex networks.Comment: Submitted to "Communication in Nonlinear Science and Numerical
Simulation
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