Ranking Influential Nodes in Complex Networks with Information Entropy Method
The ranking of influential nodes in networks is of great significance. Influential nodes play an enormous role during the evolution process of information dissemination, viral marketing, and public opinion control. The sorting method of multiple attributes is an effective way to identify the influen...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/5903798 |
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| _version_ | 1832553493334851584 |
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| author | Nan Zhao Jingjing Bao Nan Chen |
| author_facet | Nan Zhao Jingjing Bao Nan Chen |
| author_sort | Nan Zhao |
| collection | DOAJ |
| description | The ranking of influential nodes in networks is of great significance. Influential nodes play an enormous role during the evolution process of information dissemination, viral marketing, and public opinion control. The sorting method of multiple attributes is an effective way to identify the influential nodes. However, these methods offer a limited improvement in algorithm performance because diversity between different attributes is not properly considered. On the basis of the k-shell method, we propose an improved multiattribute k-shell method by using the iterative information in the decomposition process. Our work combines sigmod function and iteration information to obtain the position index. The position attribute is obtained by combining the shell value and the location index. The local information of the node is adopted to obtain the neighbor property. Finally, the position attribute and neighbor attribute are weighted by the method of information entropy weighting. The experimental simulations in six real networks combined with the SIR model and other evaluation measure fully verify the correctness and effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-b83bb392379c493cb9006fe69affdd1b |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b83bb392379c493cb9006fe69affdd1b2025-02-03T05:53:56ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/59037985903798Ranking Influential Nodes in Complex Networks with Information Entropy MethodNan Zhao0Jingjing Bao1Nan Chen2State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, ChinaState Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, ChinaState Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, ChinaThe ranking of influential nodes in networks is of great significance. Influential nodes play an enormous role during the evolution process of information dissemination, viral marketing, and public opinion control. The sorting method of multiple attributes is an effective way to identify the influential nodes. However, these methods offer a limited improvement in algorithm performance because diversity between different attributes is not properly considered. On the basis of the k-shell method, we propose an improved multiattribute k-shell method by using the iterative information in the decomposition process. Our work combines sigmod function and iteration information to obtain the position index. The position attribute is obtained by combining the shell value and the location index. The local information of the node is adopted to obtain the neighbor property. Finally, the position attribute and neighbor attribute are weighted by the method of information entropy weighting. The experimental simulations in six real networks combined with the SIR model and other evaluation measure fully verify the correctness and effectiveness of the proposed method.http://dx.doi.org/10.1155/2020/5903798 |
| spellingShingle | Nan Zhao Jingjing Bao Nan Chen Ranking Influential Nodes in Complex Networks with Information Entropy Method Complexity |
| title | Ranking Influential Nodes in Complex Networks with Information Entropy Method |
| title_full | Ranking Influential Nodes in Complex Networks with Information Entropy Method |
| title_fullStr | Ranking Influential Nodes in Complex Networks with Information Entropy Method |
| title_full_unstemmed | Ranking Influential Nodes in Complex Networks with Information Entropy Method |
| title_short | Ranking Influential Nodes in Complex Networks with Information Entropy Method |
| title_sort | ranking influential nodes in complex networks with information entropy method |
| url | http://dx.doi.org/10.1155/2020/5903798 |
| work_keys_str_mv | AT nanzhao rankinginfluentialnodesincomplexnetworkswithinformationentropymethod AT jingjingbao rankinginfluentialnodesincomplexnetworkswithinformationentropymethod AT nanchen rankinginfluentialnodesincomplexnetworkswithinformationentropymethod |