Accuracy and Precision of Methods for Community Identification in Weighted Networks
Ying Fan, Menghui Li, Peng Zhang, Jinshan Wu, Zengru Di
Abstract
Based on brief review of approaches for community identification and measurement for sensitivity characterization, the accuracy and precision of several approaches for detecting communities in weighted networks are investigated. In weighted networks, the community structure should take both links and link weights into account and the partition of networks should be evaluated by weighted modularity Qw. The results reveal that link weight has important effects on communities especially in dense networks. Potts model and Weighted Extremal Optimization (WEO) algorithm work well on weighted networks. Then Potts model and WEO algorithms are used to detect communities in Rhesus monkey network. The results gives nice understanding for real community structure.
Create a lesson
Related papers
Distinct routes to phase transitions in spatial activation systems
Jialu Zhang, Guanyu Zhang, Leyang Xue et al.
District-Level Food Environment Indicators and Social Vulnerability in São Paulo
Pedro Lemes Sixel Lobo, Eric Tokuda, Kuruvilla Joseph Abraham et al.
Prompt Sensitivity of Generative Agents: Evidence from an Epidemic Model
Ross Williams, Niyousha Hosseinichimeh
Giant strongly biconnected components of directed networks: a generating function approach
Minsoo Yang, Reinhard Laubenbacher, Byungjoon Min
(k,n)-core percolation on hypergraphs with anchor nodes
Hoseung Jang, Byungjoon Min, Ginestra Bianconi
The complex relationship between anti-immigrant sentiment and exposure in the Netherlands
Benedikt Meylahn, Tommaso Giommoni, Mike Lees et al.