Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/978-3-540-73133-7_1 | Clinical Orthopaedics and Related Research |
Keywords | Field | DocType |
generic model,arbitrary hierarchical structure,hypothesis testing,hierarchical structure,hierarchical feature,particular real-world network,network data,generic null model,community property,structural inference,example network,data analysis,random graph,null model,hypothesis test | Combinatorics,Random graph,Vertex (geometry),Inference,Computer science,Hierarchical network model,Null model,Artificial intelligence,Hierarchy,Machine learning,Statistical hypothesis testing,Hierarchical organization | Journal |
Volume | ISSN | Citations |
abs/physics/0610051 | Proc. 23rd International Conference on Machine Learning (ICML),
Workshop on Social Network Analysis, Pittsburgh PA, June 2006 | 46 |
PageRank | References | Authors |
5.44 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aaron Clauset | 1 | 2033 | 146.18 |
| Cristopher Moore | 2 | 1765 | 160.55 |
| Mark E. J. Newman | 3 | 10380 | 1003.78 |