{"title":"社团数有限的任务网络划分方法","authors":"Liang Guo, Yunjun Lu, Qian Liu, Keren Zhu, Lv Zhao","doi":"10.1145/3573834.3574518","DOIUrl":null,"url":null,"abstract":"In order to solve the problem that the number of communities in the current task network partition method cannot be determined, this paper proposes the concept and measurement method of task support degree, and takes the community to have a large internal relevance and a small external-community relevance as the optimization objective of task network partition. Based on the NSGA-II method, the concept of task partition granularity is introduced, and a task network partition method based on NSGA-II with limited community number is proposed. Finally, simulation experiments verify the feasibility of the algorithm and its advantages in terms of time consumption compared with traditional methods.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A method for task network partition with limited community number\",\"authors\":\"Liang Guo, Yunjun Lu, Qian Liu, Keren Zhu, Lv Zhao\",\"doi\":\"10.1145/3573834.3574518\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to solve the problem that the number of communities in the current task network partition method cannot be determined, this paper proposes the concept and measurement method of task support degree, and takes the community to have a large internal relevance and a small external-community relevance as the optimization objective of task network partition. Based on the NSGA-II method, the concept of task partition granularity is introduced, and a task network partition method based on NSGA-II with limited community number is proposed. Finally, simulation experiments verify the feasibility of the algorithm and its advantages in terms of time consumption compared with traditional methods.\",\"PeriodicalId\":345434,\"journal\":{\"name\":\"Proceedings of the 4th International Conference on Advanced Information Science and System\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 4th International Conference on Advanced Information Science and System\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3573834.3574518\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 4th International Conference on Advanced Information Science and System","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3573834.3574518","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A method for task network partition with limited community number
In order to solve the problem that the number of communities in the current task network partition method cannot be determined, this paper proposes the concept and measurement method of task support degree, and takes the community to have a large internal relevance and a small external-community relevance as the optimization objective of task network partition. Based on the NSGA-II method, the concept of task partition granularity is introduced, and a task network partition method based on NSGA-II with limited community number is proposed. Finally, simulation experiments verify the feasibility of the algorithm and its advantages in terms of time consumption compared with traditional methods.