Detecting overlapping community structure: Estonian network of payments
December 11, 2018 Β· Declared Dead Β· π Proceedings of the Estonian Academy of Sciences
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Stephanie RendΓ³n de la Torre, Jaan Kalda, Robert Kitt, JΓΌri Engelbrecht
arXiv ID
1812.04250
Category
physics.soc-ph
Cross-listed
cs.SI
Citations
1
Venue
Proceedings of the Estonian Academy of Sciences
Last Checked
4 months ago
Abstract
Revealing the community structure exhibited by real networks is a fundamental phase towards a comprehensive understanding of complex systems beyond the local organization of their components. Community detection techniques help on providing insights into understanding the local organization of the components of networks. In this study we identify and investigate the overlapping community structure of an interesting and unique case of study: the Estonian network of payments. In order to perform the study, we use the Clique Percolation Method and explore statistical distribution functions of the communities, where in most cases we found scale-free properties. In this network the nodes represent Estonian companies and the links represent payments done between the companies. Our study adds to the literature of complex networks by presenting the first overlapping community detection analysis of a country's network of payments.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β physics.soc-ph
π
π
The Cartographer
R.I.P.
π»
Ghosted
Networks beyond pairwise interactions: structure and dynamics
R.I.P.
π»
Ghosted
Statistical physics of human cooperation
R.I.P.
π»
Ghosted
Vital nodes identification in complex networks
R.I.P.
π»
Ghosted
Influence maximization in complex networks through optimal percolation
R.I.P.
π»
Ghosted
Scale-free networks are rare
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted