{"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}
引用次数: 0
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.