{"title":"量化社会关系的模型","authors":"Disa Sariola","doi":"10.1109/EISIC49498.2019.9108853","DOIUrl":null,"url":null,"abstract":"This article proposes a mathematical model for quantifying relationships between agents within a network based on their similarity, dissimilarity, level of friendship, group and activity status of the agent. We propose a set of functions to facilitate quantifying social dynamics. Our functions cover the comparison of an agent with group and comparing a group with groups based on their set of attributes. We also propose a model of comparison for agent vs. agent based on their attributes, features and the likelihood of attribute similarity between agents. The model employs a method of determining connection probabilities between nodes in order to find hidden connections between agents. We build on existing work in the study of social networks.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A model of quantifying social relationships\",\"authors\":\"Disa Sariola\",\"doi\":\"10.1109/EISIC49498.2019.9108853\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article proposes a mathematical model for quantifying relationships between agents within a network based on their similarity, dissimilarity, level of friendship, group and activity status of the agent. We propose a set of functions to facilitate quantifying social dynamics. Our functions cover the comparison of an agent with group and comparing a group with groups based on their set of attributes. We also propose a model of comparison for agent vs. agent based on their attributes, features and the likelihood of attribute similarity between agents. The model employs a method of determining connection probabilities between nodes in order to find hidden connections between agents. We build on existing work in the study of social networks.\",\"PeriodicalId\":117256,\"journal\":{\"name\":\"2019 European Intelligence and Security Informatics Conference (EISIC)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 European Intelligence and Security Informatics Conference (EISIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EISIC49498.2019.9108853\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 European Intelligence and Security Informatics Conference (EISIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EISIC49498.2019.9108853","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This article proposes a mathematical model for quantifying relationships between agents within a network based on their similarity, dissimilarity, level of friendship, group and activity status of the agent. We propose a set of functions to facilitate quantifying social dynamics. Our functions cover the comparison of an agent with group and comparing a group with groups based on their set of attributes. We also propose a model of comparison for agent vs. agent based on their attributes, features and the likelihood of attribute similarity between agents. The model employs a method of determining connection probabilities between nodes in order to find hidden connections between agents. We build on existing work in the study of social networks.