Research on social network influence maximization algorithm based on time sequential relationship
For the time sequential relationship between nodes in a dynamic social network,social network influence maximization based on time sequential relationship was proved.The problem was to find k nodes on a time sequential social network to maximize the spread of information.Firstly,the propagation prob...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2020-10-01
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| Series: | Tongxin xuebao |
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| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020191/ |
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| _version_ | 1841539255468294144 |
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| author | Jing CHEN Ziyi QI |
| author_facet | Jing CHEN Ziyi QI |
| author_sort | Jing CHEN |
| collection | DOAJ |
| description | For the time sequential relationship between nodes in a dynamic social network,social network influence maximization based on time sequential relationship was proved.The problem was to find k nodes on a time sequential social network to maximize the spread of information.Firstly,the propagation probability between nodes was calculated by the improved degree estimation algorithm.Secondly,in order to solve the problem that WCM models based on static social networks could not be applied to time sequential social networks,an IWCM propagation model was proposed and based on this,a two-stage time sequential social network influence maximization algorithm was proposed.The algorithm used the time sequential heuristic phase and the time sequential greedy phase to select the candidate node with the largest influence estimated value inf (u) and the most influential seeds.At last,the efficiency and accuracy of the TIM algorithm were proved by experiments.In addition,the algorithm combines the advantages of the heuristic algorithm and the greedy algorithm,reducing the calculation range of the marginal revenue from all nodes in the network to the candidate nodes,and greatly shortens the running time of the program while ensuring accuracy. |
| format | Article |
| id | doaj-art-0cdd9adeb66a48308f3c37b6fd91d672 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-10-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-0cdd9adeb66a48308f3c37b6fd91d6722025-01-14T07:21:00ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-10-014121122159738256Research on social network influence maximization algorithm based on time sequential relationshipJing CHENZiyi QIFor the time sequential relationship between nodes in a dynamic social network,social network influence maximization based on time sequential relationship was proved.The problem was to find k nodes on a time sequential social network to maximize the spread of information.Firstly,the propagation probability between nodes was calculated by the improved degree estimation algorithm.Secondly,in order to solve the problem that WCM models based on static social networks could not be applied to time sequential social networks,an IWCM propagation model was proposed and based on this,a two-stage time sequential social network influence maximization algorithm was proposed.The algorithm used the time sequential heuristic phase and the time sequential greedy phase to select the candidate node with the largest influence estimated value inf (u) and the most influential seeds.At last,the efficiency and accuracy of the TIM algorithm were proved by experiments.In addition,the algorithm combines the advantages of the heuristic algorithm and the greedy algorithm,reducing the calculation range of the marginal revenue from all nodes in the network to the candidate nodes,and greatly shortens the running time of the program while ensuring accuracy.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020191/time sequential social networkinfluence maximizationinformation propagation modelgreedy algorithmheuristic algorithm |
| spellingShingle | Jing CHEN Ziyi QI Research on social network influence maximization algorithm based on time sequential relationship Tongxin xuebao time sequential social network influence maximization information propagation model greedy algorithm heuristic algorithm |
| title | Research on social network influence maximization algorithm based on time sequential relationship |
| title_full | Research on social network influence maximization algorithm based on time sequential relationship |
| title_fullStr | Research on social network influence maximization algorithm based on time sequential relationship |
| title_full_unstemmed | Research on social network influence maximization algorithm based on time sequential relationship |
| title_short | Research on social network influence maximization algorithm based on time sequential relationship |
| title_sort | research on social network influence maximization algorithm based on time sequential relationship |
| topic | time sequential social network influence maximization information propagation model greedy algorithm heuristic algorithm |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020191/ |
| work_keys_str_mv | AT jingchen researchonsocialnetworkinfluencemaximizationalgorithmbasedontimesequentialrelationship AT ziyiqi researchonsocialnetworkinfluencemaximizationalgorithmbasedontimesequentialrelationship |