{"title":"使用数学模型对犯罪网络成员进行排序","authors":"Lucas Chirino","doi":"10.1137/17s016592","DOIUrl":null,"url":null,"abstract":"Different mathematical approaches to ranking were used to determine the level of importance of every member in a criminal network. Two different data sets consisting of phone records provided by the FBI were analyzed. The first data set consisted of call logs over a three year span of members in a drug ring. The second data set consisted of the call logs of members in a gang. After analyzing the results of the rankings, properties of the two networks are discussed to provide further insight into why certain ranking algorithms performed well and others did not.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using Mathematical Models to Rank the Members of Criminal Networks\",\"authors\":\"Lucas Chirino\",\"doi\":\"10.1137/17s016592\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Different mathematical approaches to ranking were used to determine the level of importance of every member in a criminal network. Two different data sets consisting of phone records provided by the FBI were analyzed. The first data set consisted of call logs over a three year span of members in a drug ring. The second data set consisted of the call logs of members in a gang. After analyzing the results of the rankings, properties of the two networks are discussed to provide further insight into why certain ranking algorithms performed well and others did not.\",\"PeriodicalId\":93373,\"journal\":{\"name\":\"SIAM undergraduate research online\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SIAM undergraduate research online\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1137/17s016592\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIAM undergraduate research online","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1137/17s016592","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using Mathematical Models to Rank the Members of Criminal Networks
Different mathematical approaches to ranking were used to determine the level of importance of every member in a criminal network. Two different data sets consisting of phone records provided by the FBI were analyzed. The first data set consisted of call logs over a three year span of members in a drug ring. The second data set consisted of the call logs of members in a gang. After analyzing the results of the rankings, properties of the two networks are discussed to provide further insight into why certain ranking algorithms performed well and others did not.