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Dear colleagues,

 

Perhaps some of you already know AERCS
(http://bosch.informatik.rwth-aachen.de:5080/AERCS). AERCS is a
visualization tool for communities of academic events in Computer Science.
It is developed and hosted by the Advanced Community Information Systems
(ACIS) group (http://dbis.rwth-aachen.de/cms/research/ACIS) at  RWTH Aachen
University, Germany. Currently, conferences and journals from the DBLP
dataset (http://www.informatik.uni-trier.de/~ley/db/) are visualized. Other
data sets will be integrated in the future.  While the DBLP bibliographic
data set is quite comprehensive it does not show all publications!

 

Now we are offering a new service for comparing journals and conference in
computer science by means of dynamic network analysis  (
<http://bosch.informatik.rwth-aachen.de:5080/AERCS/selectSeriesComparison.js
p>
http://bosch.informatik.rwth-aachen.de:5080/AERCS/selectSeriesComparison.jsp
).

Dynamic network analysis uses time series analysis of networks to explore
dynamic patterns of networks such as growth and decay. A typical result of
our research is the dynamic comparison of computer science communities. A
computer science conference or journal can be compared by time series
analysis for typical social network growth patterns like density, diameter,
clustering coefficient, largest connected component, average path length and
maximum betweenness. 

 

In a co-authorship network, each node represents an author, and each edge
represents one or more co-authored papers by the two connected nodes. The
co-authorship network therefore represents the author community of a
conference series or journal. Each data point in a series plot represents
one "event". For conferences, each data point represents a specific
conference event (e.g. VLDB 2009 in the VLDB conference series), while for
journals each data point refers to a particular volume (e.g. Volume 2 of
IEEE Software). The horizontal axis represents an ordered history of events
from oldest (left) to newest (right).

 

E.g. for the co-authorship network, the emergence of the giant component
(largest component) indicates the cohesiveness of collaboration within the
community, while the betweenness shows the existence of gatekeepers and
their importance. The clustering coefficient measures the extent to which
the community is clustered into sub-communities. Other parameters such as
diameter and average shortest path length, show whether the community is
still developing or whether it is stable. 

 

Everybody can use this service. Just visit AERCS and test it out! Your
feedback is very much welcome.

 

This service is brought to you by the chair of information systems and
databases at RWTH Aachen University.

 

Sincerely,

Ralf Klamma

 

 

--

PD Dr. Ralf Klamma, AOR  <mailto:[log in to unmask]>
mailto:[log in to unmask] 

  RWTH Aachen   www   : http://dbis.rwth-aachen.de/cms/staff/klamma 

  Informatik 5  phone : +492418021513,+491735228052 fax: +492418022321

    DBIS        mail  : Informatik 5, Ahornstr. 55, 52056 Aachen 

 


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