2006/10/09 by Aaron Clauset, Cristopher Moore, M. E. J. Newman
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Opinion Dynamics and Social Influence #cs.LG #physics.data-an #physics.soc-ph
paper · pdf · doi:10.1007/978-3-540-73133-7_1
published as Proc. 23rd International Conference on Machine Learning (ICML), Workshop on Social Network Analysis, Pittsburgh PA, June 2006 · 8 pages, 8 figures
arxiv created 2006/10/09 · openalex publication_date 2008/04/11 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
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.