Print

Print


***** To join INSNA, visit http://www.insna.org *****

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.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]

  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

 

_____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.