On the Stability of Community Detection Algorithms on Longitudinal Citation Data

Details On the Stability of Community Detection Algorithms on Longitudinal Citation Data On the Stability of Community Detection Algorithms on Longitudinal Citation Data

2009-08-04

arxiv.org/abs/0908.0449v2

Physics

Physics and Society

17 pages, 7 figures, presenting at Applications of Social Network Analysis 2009, ETH Zurich Edit, August 17, 2009: updated abs

Scientific paper

There are fundamental differences between citation networks and other classes of graphs. In particular, given that citation networks are directed and acyclic, methods developed primarily for use with undirected social network data may face obstacles. This is particularly true for the dynamic development of community structure in citation networks. Namely, it is neither clear when it is appropriate to employ existing community detection approaches nor is it clear how to choose among existing approaches. Using simulated data, we attempt to clarify the conditions under which one should use existing methods and which of these algorithms is appropriate in a given context. We hope this paper will serve as both a useful guidepost and an encouragement to those interested in the development of more targeted approaches for use with longitudinal citation data.

Affiliated with

Also associated with

No associations

Rating

On the Stability of Community Detection Algorithms on Longitudinal Citation Data does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with On the Stability of Community Detection Algorithms on Longitudinal Citation Data, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and On the Stability of Community Detection Algorithms on Longitudinal Citation Data will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-557154