Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/230782 
Year of Publication: 
2019
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2019-006
Publisher: 
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
Abstract: 
Understanding the topological structure of real world networks is of huge interest in a variety of fields. One of the way to investigate this structure is to find the groups of densely connected nodes called communities. This paper presents a new non-parametric method of community detection in networks called Adaptive Weights Community Detection. The idea of the algorithm is to associate a local community for each node. On every iteration the algorithm tests a hypothesis that two nodes are in the same community by comparing their local communities. The test rejects the hypothesis if the density of edges between these two local communities is lower than the density inside each one. A detailed performance analysis of the method shows its dominance over state-of- the-art methods on well known artificial and real world benchmarks.
Subjects: 
Adaptive weights
Gap coefficient
Graph clustering
Nonparametric
Overlapping communities
JEL: 
C00
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.