Network community detection via iterative edge removal in a flocking-like system
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2018
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908786667552768 |
|---|---|
| author | Verri, Filipe Alves Neto Gueleri, Roberto Alves Zheng, Qiusheng Zhang, Junbao Zhao, Liang |
| author_facet | Verri, Filipe Alves Neto Gueleri, Roberto Alves Zheng, Qiusheng Zhang, Junbao Zhao, Liang |
| contents | We present a network community-detection technique based on properties that emerge from a nature-inspired system of aligning particles. Initially, each vertex is assigned a random-direction unit vector. A nonlinear dynamic law is established so that neighboring vertices try to become aligned with each other. After some time, the system stops and edges that connect the least-aligned pairs of vertices are removed. Then the evolution starts over without the removed edges, and after enough number of removal rounds, each community becomes a connected component. The proposed approach is evaluated using widely-accepted benchmarks and real-world networks. Experimental results reveal that the method is robust and excels on a wide variety of networks. Moreover, for large sparse networks, the edge-removal process runs in quasilinear time, which enables application in large-scale networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1802_04186 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Network community detection via iterative edge removal in a flocking-like system Verri, Filipe Alves Neto Gueleri, Roberto Alves Zheng, Qiusheng Zhang, Junbao Zhao, Liang Social and Information Networks Physics and Society We present a network community-detection technique based on properties that emerge from a nature-inspired system of aligning particles. Initially, each vertex is assigned a random-direction unit vector. A nonlinear dynamic law is established so that neighboring vertices try to become aligned with each other. After some time, the system stops and edges that connect the least-aligned pairs of vertices are removed. Then the evolution starts over without the removed edges, and after enough number of removal rounds, each community becomes a connected component. The proposed approach is evaluated using widely-accepted benchmarks and real-world networks. Experimental results reveal that the method is robust and excels on a wide variety of networks. Moreover, for large sparse networks, the edge-removal process runs in quasilinear time, which enables application in large-scale networks. |
| title | Network community detection via iterative edge removal in a flocking-like system |
| topic | Social and Information Networks Physics and Society |
| url | https://arxiv.org/abs/1802.04186 |