Structural Inference of Hierarchies in Networks

Details Structural Inference of Hierarchies in Networks Structural Inference of Hierarchies in Networks

2006-10-09

arxiv.org/abs/physics/0610051v1

Proc. 23rd International Conference on Machine Learning (ICML), Workshop on Social Network Analysis, Pittsburgh PA, June 2006

Physics

Physics and Society

8 pages, 8 figures

Scientific paper

10.1007/978-3-540-73133-7_1

One property of networks that has received comparatively little attention is hierarchy, i.e., the property of having vertices that cluster together in groups, which then join to form groups of groups, and so forth, up through all levels of organization in the network. Here, we give a precise definition of hierarchical structure, give a generic model for generating arbitrary hierarchical structure in a random graph, and describe a statistically principled way to learn the set of hierarchical features that most plausibly explain a particular real-world network. By applying this approach to two example networks, we demonstrate its advantages for the interpretation of network data, the annotation of graphs with edge, vertex and community properties, and the generation of generic null models for further hypothesis testing.

Affiliated with

Also associated with

No associations

Rating

Structural Inference of Hierarchies in Networks 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 Structural Inference of Hierarchies in Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Structural Inference of Hierarchies in Networks will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-343529