A Multi-Granularity Backbone Network Extraction Method Based on the Topology Potential
Inspired by the theory of physics field, in this paper, we propose a novel backbone network compression algorithm based on topology potential. With consideration of the network connectivity and backbone compression precision, the method is flexible and efficient according to various network characte...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/8604132 |
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| _version_ | 1850165834519937024 |
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| author | Hanning Yuan Yanni Han Ning Cai Wei An |
| author_facet | Hanning Yuan Yanni Han Ning Cai Wei An |
| author_sort | Hanning Yuan |
| collection | DOAJ |
| description | Inspired by the theory of physics field, in this paper, we propose a novel backbone network compression algorithm based on topology potential. With consideration of the network connectivity and backbone compression precision, the method is flexible and efficient according to various network characteristics. Meanwhile, we define a metric named compression ratio to evaluate the performance of backbone networks, which provides an optimal extraction granularity based on the contributions of degree number and topology connectivity. We apply our method to the public available Internet AS network and Hep-th network, which are the public datasets in the field of complex network analysis. Furthermore, we compare the obtained results with the metrics of precision ratio and recall ratio. All these results show that our algorithm is superior to the compared methods. Moreover, we investigate the characteristics in terms of degree distribution and self-similarity of the extracted backbone. It is proven that the compressed backbone network has a lot of similarity properties to the original network in terms of power-law exponent. |
| format | Article |
| id | doaj-art-e1dd3fd1455b46a5a8bf2b061e907daf |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e1dd3fd1455b46a5a8bf2b061e907daf2025-08-20T02:21:38ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/86041328604132A Multi-Granularity Backbone Network Extraction Method Based on the Topology PotentialHanning Yuan0Yanni Han1Ning Cai2Wei An3International School of Software, Beijing Institute of Technology, Beijing 100081, ChinaInstitute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, ChinaInstitute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, ChinaInstitute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, ChinaInspired by the theory of physics field, in this paper, we propose a novel backbone network compression algorithm based on topology potential. With consideration of the network connectivity and backbone compression precision, the method is flexible and efficient according to various network characteristics. Meanwhile, we define a metric named compression ratio to evaluate the performance of backbone networks, which provides an optimal extraction granularity based on the contributions of degree number and topology connectivity. We apply our method to the public available Internet AS network and Hep-th network, which are the public datasets in the field of complex network analysis. Furthermore, we compare the obtained results with the metrics of precision ratio and recall ratio. All these results show that our algorithm is superior to the compared methods. Moreover, we investigate the characteristics in terms of degree distribution and self-similarity of the extracted backbone. It is proven that the compressed backbone network has a lot of similarity properties to the original network in terms of power-law exponent.http://dx.doi.org/10.1155/2018/8604132 |
| spellingShingle | Hanning Yuan Yanni Han Ning Cai Wei An A Multi-Granularity Backbone Network Extraction Method Based on the Topology Potential Complexity |
| title | A Multi-Granularity Backbone Network Extraction Method Based on the Topology Potential |
| title_full | A Multi-Granularity Backbone Network Extraction Method Based on the Topology Potential |
| title_fullStr | A Multi-Granularity Backbone Network Extraction Method Based on the Topology Potential |
| title_full_unstemmed | A Multi-Granularity Backbone Network Extraction Method Based on the Topology Potential |
| title_short | A Multi-Granularity Backbone Network Extraction Method Based on the Topology Potential |
| title_sort | multi granularity backbone network extraction method based on the topology potential |
| url | http://dx.doi.org/10.1155/2018/8604132 |
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