46 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
Experiments on comparing graph clusterins
A promising approach to compare graph clusterings is based on
using measurements for calculating the distance. Existing
measures either use the structure of clusterings or
quality--based aspects. Each approach suffers from critical
drawbacks. We introduce a new approach combining both aspects
and leading to better results for comparing graph clusterings.
An experimental evaluation of existing and new measures shows
that the significant drawbacks of existing techniques are not
only theoretical in nature and proves that the results of our
new measures are more coherent with intuition
A new paradigm for complex network visualization
We propose a new layout paradigm for drawing a nested
decomposition of a large network. The visualization supports the
recognition of abstract features of the decomposition, while
drawing all elements. In order to support the visual analysis
that focuses on the dependencies of the individual parts of the
decomposition, we use an annulus as the general underlying
shape. This method has been evaluated using real world data and
offers surprising readability
Analysis of the autonomous system network and of overlay networks using visualization
Taking the physical Internet at the Autonomous System (AS) level
as an instance of a complex network, and Gnutella as a popular
peer-to-peer application running on top of it, we investigated
the correlation of overlay networks with their underlying
topology using visualization. We find that while overlay
networks create arbitrary topologies, they differ from randomly
generated networks, and there is a correlation with the
underlying network. In addition, we successfully validated the
applicability of our visualization technique for AS topologies
by comparing Routeviews data sets with DIMES data sets, and by
analyzing the temporal evolution in the Routeviews data sets
On Modularity - NP-Completeness and Beyond
Modularity is a recently introduced quality measure for graph
clusterings. It has immediately received considerable attention
in several disciplines, and in particular in the complex
systems literature, although its properties are not well
understood. We here present first results on the computational
and analytical properties of modularity. The complexity status
of modularity maximization is resolved showing that the
corresponding decision version is NP-complete in the strong
sense. We also give a formulation as an Integer Linear Program
(ILP) to facilitate exact optimization, and provide results on
the approximation factor of the commonly used greedy algorithm.
Completing our investigation, we characterize clusterings with
maximum Modularity for several graph families