{"title":"利用全球多样性和地方特征识别有影响力的社交网络传播者","authors":"Yu-Hsiang Fu, Chung-Yuan Huang, Chuen-Tsai Sun","doi":"10.1109/ASONAM.2014.6921700","DOIUrl":null,"url":null,"abstract":"The identification of influential spreaders of information via social networks can assist in the acceleration or hindrance of information dissemination, in increased product exposure, and in the detection of contagious disease outbreaks. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, researchers have overlooked node diversity within network structures as a means of measuring spreading ability. The two-step framework described in this paper uses a robust and insensitive measure that combines global diversity and local features (e.g., degree centrality) to identify the most influential social network nodes. Preliminary experiment results indicate that the proposed method performs well and maintains stability in single initial spreader scenarios associated with different social network datasets.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Using global diversity and local features to identify influential social network spreaders\",\"authors\":\"Yu-Hsiang Fu, Chung-Yuan Huang, Chuen-Tsai Sun\",\"doi\":\"10.1109/ASONAM.2014.6921700\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The identification of influential spreaders of information via social networks can assist in the acceleration or hindrance of information dissemination, in increased product exposure, and in the detection of contagious disease outbreaks. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, researchers have overlooked node diversity within network structures as a means of measuring spreading ability. The two-step framework described in this paper uses a robust and insensitive measure that combines global diversity and local features (e.g., degree centrality) to identify the most influential social network nodes. Preliminary experiment results indicate that the proposed method performs well and maintains stability in single initial spreader scenarios associated with different social network datasets.\",\"PeriodicalId\":143584,\"journal\":{\"name\":\"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-08-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASONAM.2014.6921700\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASONAM.2014.6921700","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using global diversity and local features to identify influential social network spreaders
The identification of influential spreaders of information via social networks can assist in the acceleration or hindrance of information dissemination, in increased product exposure, and in the detection of contagious disease outbreaks. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, researchers have overlooked node diversity within network structures as a means of measuring spreading ability. The two-step framework described in this paper uses a robust and insensitive measure that combines global diversity and local features (e.g., degree centrality) to identify the most influential social network nodes. Preliminary experiment results indicate that the proposed method performs well and maintains stability in single initial spreader scenarios associated with different social network datasets.