Detection of dynamic communities in temporal networks with sparse data
Abstract Temporal networks are a powerful tool for studying the dynamic nature of a wide range of real-world complex systems, including social, biological and physical systems. In particular, detection of dynamic communities within these networks can help identify important cohesive structures and f...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
|
| Series: | Applied Network Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s41109-024-00687-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841544876572803072 |
|---|---|
| author | Nataša Djurdjevac Conrad Elisa Tonello Johannes Zonker Heike Siebert |
| author_facet | Nataša Djurdjevac Conrad Elisa Tonello Johannes Zonker Heike Siebert |
| author_sort | Nataša Djurdjevac Conrad |
| collection | DOAJ |
| description | Abstract Temporal networks are a powerful tool for studying the dynamic nature of a wide range of real-world complex systems, including social, biological and physical systems. In particular, detection of dynamic communities within these networks can help identify important cohesive structures and fundamental mechanisms driving systems behaviour. However, when working with real-world systems, available data is often limited and sparse, due to missing data on systems entities, their evolution and interactions, as well as uncertainty regarding temporal resolution. This can hinder accurate representation of the system over time and result in incomplete or biased community dynamics. In this paper, we consider established methods for community detection and, using synthetic data experiments and real-world case studies, we evaluate the impact of data sparsity on the quality of identified dynamic communities. Our results give valuable insights on the evolution of systems with sparse data, which are less studied in existing literature, but are frequently encountered in real-world applications. |
| format | Article |
| id | doaj-art-245353405f9f4444a35af3512a7cc437 |
| institution | Kabale University |
| issn | 2364-8228 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj-art-245353405f9f4444a35af3512a7cc4372025-01-12T12:11:54ZengSpringerOpenApplied Network Science2364-82282025-01-0110112910.1007/s41109-024-00687-3Detection of dynamic communities in temporal networks with sparse dataNataša Djurdjevac Conrad0Elisa Tonello1Johannes Zonker2Heike Siebert3Modeling and Simulation of Complex Processes, Zuse Institute BerlinDepartment of Mathematics and Computer Science, Freie Universität BerlinModeling and Simulation of Complex Processes, Zuse Institute BerlinDepartment of Mathematics, Kiel UniversityAbstract Temporal networks are a powerful tool for studying the dynamic nature of a wide range of real-world complex systems, including social, biological and physical systems. In particular, detection of dynamic communities within these networks can help identify important cohesive structures and fundamental mechanisms driving systems behaviour. However, when working with real-world systems, available data is often limited and sparse, due to missing data on systems entities, their evolution and interactions, as well as uncertainty regarding temporal resolution. This can hinder accurate representation of the system over time and result in incomplete or biased community dynamics. In this paper, we consider established methods for community detection and, using synthetic data experiments and real-world case studies, we evaluate the impact of data sparsity on the quality of identified dynamic communities. Our results give valuable insights on the evolution of systems with sparse data, which are less studied in existing literature, but are frequently encountered in real-world applications.https://doi.org/10.1007/s41109-024-00687-3Temporal networksSparse dataDynamic communitiesTemporal resolutionTemporal clustering |
| spellingShingle | Nataša Djurdjevac Conrad Elisa Tonello Johannes Zonker Heike Siebert Detection of dynamic communities in temporal networks with sparse data Applied Network Science Temporal networks Sparse data Dynamic communities Temporal resolution Temporal clustering |
| title | Detection of dynamic communities in temporal networks with sparse data |
| title_full | Detection of dynamic communities in temporal networks with sparse data |
| title_fullStr | Detection of dynamic communities in temporal networks with sparse data |
| title_full_unstemmed | Detection of dynamic communities in temporal networks with sparse data |
| title_short | Detection of dynamic communities in temporal networks with sparse data |
| title_sort | detection of dynamic communities in temporal networks with sparse data |
| topic | Temporal networks Sparse data Dynamic communities Temporal resolution Temporal clustering |
| url | https://doi.org/10.1007/s41109-024-00687-3 |
| work_keys_str_mv | AT natasadjurdjevacconrad detectionofdynamiccommunitiesintemporalnetworkswithsparsedata AT elisatonello detectionofdynamiccommunitiesintemporalnetworkswithsparsedata AT johanneszonker detectionofdynamiccommunitiesintemporalnetworkswithsparsedata AT heikesiebert detectionofdynamiccommunitiesintemporalnetworkswithsparsedata |