Spectral Multi-Scale Community Detection in Temporal Networks with an Application. Kuncheva, Z. and Montana, G.
Spectral Multi-Scale Community Detection in Temporal Networks with an Application [link]Paper  abstract   bibtex   
The analysis of temporal networks has a wide area of applications in a world of technological advances. An important aspect of temporal network analysis is the discovery of community structures. Real data networks are often very large and the communities are observed to have a hierarchical structure referred to as multi-scale communities. Changes in the community structure over time might take place either at one scale or across all scales of the community structure. The multilayer formulation of the modularity maximization (MM) method introduced captures the changing multi-scale community structure of temporal networks. This method introduces a coupling between communities in neighboring time layers by allowing inter-layer connections, while different values of the resolution parameter enable the detection of multi-scale communities. However, the range of this parameter's values must be manually selected. When dealing with real life data, communities at one or more scales can go undiscovered if appropriate parameter ranges are not selected. A novel Temporal Multi-scale Community Detection (TMSCD) method overcomes the obstacles mentioned above. This is achieved by using the spectral properties of the temporal network represented as a multilayer network. In this framework we select automatically the range of relevant scales within which multi-scale community partitions are sought.
@article{kunchevaSpectralMultiscaleCommunity2019,
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1901.10521},
  primaryClass = {physics, stat},
  title = {Spectral {{Multi}}-Scale {{Community Detection}} in {{Temporal Networks}} with an {{Application}}},
  url = {http://arxiv.org/abs/1901.10521},
  abstract = {The analysis of temporal networks has a wide area of applications in a world of technological advances. An important aspect of temporal network analysis is the discovery of community structures. Real data networks are often very large and the communities are observed to have a hierarchical structure referred to as multi-scale communities. Changes in the community structure over time might take place either at one scale or across all scales of the community structure. The multilayer formulation of the modularity maximization (MM) method introduced captures the changing multi-scale community structure of temporal networks. This method introduces a coupling between communities in neighboring time layers by allowing inter-layer connections, while different values of the resolution parameter enable the detection of multi-scale communities. However, the range of this parameter's values must be manually selected. When dealing with real life data, communities at one or more scales can go undiscovered if appropriate parameter ranges are not selected. A novel Temporal Multi-scale Community Detection (TMSCD) method overcomes the obstacles mentioned above. This is achieved by using the spectral properties of the temporal network represented as a multilayer network. In this framework we select automatically the range of relevant scales within which multi-scale community partitions are sought.},
  urldate = {2019-01-31},
  date = {2019-01-29},
  keywords = {Statistics - Machine Learning,Physics - Physics and Society,Computer Science - Social and Information Networks,Computer Science - Machine Learning},
  author = {Kuncheva, Zhana and Montana, Giovanni},
  file = {/home/dimitri/Nextcloud/Zotero/storage/2ZF7B936/Kuncheva and Montana - 2019 - Spectral Multi-scale Community Detection in Tempor.pdf;/home/dimitri/Nextcloud/Zotero/storage/424VQEEI/1901.html}
}
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