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Discovering network structure beyond communities
- Source :
- Scientific Reports
- Publication Year :
- 2011
-
Abstract
- To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.<br />Comment: Software implementing the method described in the paper is available at http://purl.oclc.org/net/find_structural_groups and is accompanied by a demo video available at http://www.nature.com/srep/2011/111109/srep00151/extref/srep00151-s2.mov
- Subjects :
- FOS: Computer and information sciences
Physics - Physics and Society
Theoretical computer science
Computer science
media_common.quotation_subject
Complex system
FOS: Physical sciences
Network structure
Physics and Society (physics.soc-ph)
01 natural sciences
Article
010305 fluids & plasmas
0103 physical sciences
010306 general physics
Function (engineering)
media_common
Social and Information Networks (cs.SI)
Multidisciplinary
Group (mathematics)
Computer Science - Social and Information Networks
Disordered Systems and Neural Networks (cond-mat.dis-nn)
Condensed Matter - Disordered Systems and Neural Networks
Complex network
Nonlinear Sciences - Adaptation and Self-Organizing Systems
Adaptation and Self-Organizing Systems (nlin.AO)
Internal organization
Subjects
Details
- ISSN :
- 20452322
- Volume :
- 1
- Database :
- OpenAIRE
- Journal :
- Scientific reports
- Accession number :
- edsair.doi.dedup.....ef1c85b8f386d65a9ddac44cb9e4aa0f