Abigail Z. Jacobs, Samuel F. Way, J. Ugander, A. Clauset
{"title":"整合facebook:利用异质性来理解在线社交网络整合","authors":"Abigail Z. Jacobs, Samuel F. Way, J. Ugander, A. Clauset","doi":"10.1145/2786451.2786477","DOIUrl":null,"url":null,"abstract":"Online social networks represent a popular and diverse class of social media systems. Despite this variety, each of these systems undergoes a general process of online social network assembly, which represents the complicated and heterogeneous changes that transform newly born systems into mature platforms. However, little is known about this process. For example, how much of a network's assembly is driven by simple growth? How does a network's structure change as it matures? How does network structure vary with adoption rates and user heterogeneity, and do these properties play different roles at different points in the assembly? We investigate these and other questions using a unique dataset of online connections among the roughly one million users at the first 100 colleges admitted to Facebook, captured just 20 months after its launch. We first show that different vintages and adoption rates across this population of networks reveal temporal dynamics of the assembly process, and that assembly is only loosely related to network growth. We then exploit natural experiments embedded in this dataset and complementary data obtained via Internet archaeology to show that different subnetworks matured at different rates toward similar end states. These results shed light on the processes and patterns of online social network assembly, and may facilitate more effective design for online social systems.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"33","resultStr":"{\"title\":\"Assembling thefacebook: Using Heterogeneity to Understand Online Social Network Assembly\",\"authors\":\"Abigail Z. Jacobs, Samuel F. Way, J. Ugander, A. Clauset\",\"doi\":\"10.1145/2786451.2786477\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Online social networks represent a popular and diverse class of social media systems. Despite this variety, each of these systems undergoes a general process of online social network assembly, which represents the complicated and heterogeneous changes that transform newly born systems into mature platforms. However, little is known about this process. For example, how much of a network's assembly is driven by simple growth? How does a network's structure change as it matures? How does network structure vary with adoption rates and user heterogeneity, and do these properties play different roles at different points in the assembly? We investigate these and other questions using a unique dataset of online connections among the roughly one million users at the first 100 colleges admitted to Facebook, captured just 20 months after its launch. We first show that different vintages and adoption rates across this population of networks reveal temporal dynamics of the assembly process, and that assembly is only loosely related to network growth. We then exploit natural experiments embedded in this dataset and complementary data obtained via Internet archaeology to show that different subnetworks matured at different rates toward similar end states. These results shed light on the processes and patterns of online social network assembly, and may facilitate more effective design for online social systems.\",\"PeriodicalId\":93136,\"journal\":{\"name\":\"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference\",\"volume\":\"2 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"33\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2786451.2786477\",\"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 ... ACM Web Science Conference. ACM Web Science Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2786451.2786477","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Assembling thefacebook: Using Heterogeneity to Understand Online Social Network Assembly
Online social networks represent a popular and diverse class of social media systems. Despite this variety, each of these systems undergoes a general process of online social network assembly, which represents the complicated and heterogeneous changes that transform newly born systems into mature platforms. However, little is known about this process. For example, how much of a network's assembly is driven by simple growth? How does a network's structure change as it matures? How does network structure vary with adoption rates and user heterogeneity, and do these properties play different roles at different points in the assembly? We investigate these and other questions using a unique dataset of online connections among the roughly one million users at the first 100 colleges admitted to Facebook, captured just 20 months after its launch. We first show that different vintages and adoption rates across this population of networks reveal temporal dynamics of the assembly process, and that assembly is only loosely related to network growth. We then exploit natural experiments embedded in this dataset and complementary data obtained via Internet archaeology to show that different subnetworks matured at different rates toward similar end states. These results shed light on the processes and patterns of online social network assembly, and may facilitate more effective design for online social systems.