Abnormal link detection algorithm based on semi-local structure
With the research in network science, real networks involved are becoming more and more extensive.Redundant error relationships in complex systems, or behaviors that occur deliberately for unusual purposes, such as wrong clicks on webpages, telecommunication network spying calls, have a significant...
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2022-02-01
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| Series: | 网络与信息安全学报 |
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| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2021040 |
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| _version_ | 1841529767085473792 |
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| author | Haoran SHI Lixin JI Shuxin LIU Gengrun WANG |
| author_facet | Haoran SHI Lixin JI Shuxin LIU Gengrun WANG |
| author_sort | Haoran SHI |
| collection | DOAJ |
| description | With the research in network science, real networks involved are becoming more and more extensive.Redundant error relationships in complex systems, or behaviors that occur deliberately for unusual purposes, such as wrong clicks on webpages, telecommunication network spying calls, have a significant impact on the analysis work based on network structure.As an important branch of graph anomaly detection, anomalous edge recognition in complex networks aims to identify abnormal edges in network structures caused by human fabrication or data collection errors.Existing methods mainly start from the perspective of structural similarity, and use the connected structure between nodes to evaluate the abnormal degree of edge connection, which easily leads to the decomposition of the network structure, and the detection accuracy is greatly affected by the network type.In response to this problem, a CNSCL algorithm was proposed, which calculated the node importance at the semi-local structure scale, analyzed different types of local structures, and quantified the contribution of edges to the overall network connectivity according to the semi-local centrality in different structures, and quantified the reliability of the edge connection by combining with the difference of node structure similarity.Since the connected edges need to be removed in the calculation process to measure the impact on the overall connectivity of the network, there was a problem that the importance of nodes needed to be repeatedly calculated.Therefore, in the calculation process, the proposed algorithm also designs a dynamic update method to reduce the computational complexity of the algorithm, so that it could be applied to large-scale networks.Compared with the existing methods on 7 real networks with different structural tightness, the experimental results show that the method has higher detection accuracy than the benchmark method under the AUC measure, and under the condition of network sparse or missing, It can still maintain a relatively stable recognition accuracy. |
| format | Article |
| id | doaj-art-13033448ed494bbfb54a0f5b37056702 |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2022-02-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-13033448ed494bbfb54a0f5b370567022025-01-15T03:15:38ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2022-02-018637259571455Abnormal link detection algorithm based on semi-local structureHaoran SHILixin JIShuxin LIUGengrun WANGWith the research in network science, real networks involved are becoming more and more extensive.Redundant error relationships in complex systems, or behaviors that occur deliberately for unusual purposes, such as wrong clicks on webpages, telecommunication network spying calls, have a significant impact on the analysis work based on network structure.As an important branch of graph anomaly detection, anomalous edge recognition in complex networks aims to identify abnormal edges in network structures caused by human fabrication or data collection errors.Existing methods mainly start from the perspective of structural similarity, and use the connected structure between nodes to evaluate the abnormal degree of edge connection, which easily leads to the decomposition of the network structure, and the detection accuracy is greatly affected by the network type.In response to this problem, a CNSCL algorithm was proposed, which calculated the node importance at the semi-local structure scale, analyzed different types of local structures, and quantified the contribution of edges to the overall network connectivity according to the semi-local centrality in different structures, and quantified the reliability of the edge connection by combining with the difference of node structure similarity.Since the connected edges need to be removed in the calculation process to measure the impact on the overall connectivity of the network, there was a problem that the importance of nodes needed to be repeatedly calculated.Therefore, in the calculation process, the proposed algorithm also designs a dynamic update method to reduce the computational complexity of the algorithm, so that it could be applied to large-scale networks.Compared with the existing methods on 7 real networks with different structural tightness, the experimental results show that the method has higher detection accuracy than the benchmark method under the AUC measure, and under the condition of network sparse or missing, It can still maintain a relatively stable recognition accuracy.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2021040complex networkgraph-based anomaly detectionabnormal link detectionrobustness |
| spellingShingle | Haoran SHI Lixin JI Shuxin LIU Gengrun WANG Abnormal link detection algorithm based on semi-local structure 网络与信息安全学报 complex network graph-based anomaly detection abnormal link detection robustness |
| title | Abnormal link detection algorithm based on semi-local structure |
| title_full | Abnormal link detection algorithm based on semi-local structure |
| title_fullStr | Abnormal link detection algorithm based on semi-local structure |
| title_full_unstemmed | Abnormal link detection algorithm based on semi-local structure |
| title_short | Abnormal link detection algorithm based on semi-local structure |
| title_sort | abnormal link detection algorithm based on semi local structure |
| topic | complex network graph-based anomaly detection abnormal link detection robustness |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2021040 |
| work_keys_str_mv | AT haoranshi abnormallinkdetectionalgorithmbasedonsemilocalstructure AT lixinji abnormallinkdetectionalgorithmbasedonsemilocalstructure AT shuxinliu abnormallinkdetectionalgorithmbasedonsemilocalstructure AT gengrunwang abnormallinkdetectionalgorithmbasedonsemilocalstructure |