Activity correlation spectroscopy: a novel method for inferring social relationships from activity data

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• 作者：Xuhong Zhang ; Carter T. Butts
• 关键词：Social networks ; Spectroscopy ; Activity data ; Network measurement ; Relationship detection ; Mobile devices
• 刊名：Social Network Analysis and Mining
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：7
• 期：1
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Data Mining and Knowledge Discovery; Applications of Graph Theory and Complex Networks; Game Theory, Economics, Social and Behav. Sciences; Statistics for Social Science, Behavorial Science, Education
• 出版者：Springer Vienna
• ISSN：1869-5469
• 卷排序：7

文摘

Inferring social relationships among individuals is a difficult problem, typically requiring either intrusive measurements (e.g., surveys) or convenience samples (e.g., ties captured by online social networking systems). Automatically collected activity data from mobile devices or similar sources provide a promising alternative for inexpensive and unobtrusive detection of interpersonal relationships. Here, we introduce a new method—activity correlation spectroscopy—for inferring relationships by exploiting the spectral and distributional structure of activity correlation within dyads. Unlike existing techniques, our approach can be employed with minimal, individual-level (i.e., non-relational), and non-identifying data that are easily collected using commodity hardware. We demonstrate our methodology via an application to detection of friendship and group co-membership using mobile device and survey data from the MIT Reality Mining study (Eagle et al. in Proc Natl Acad Sci 106:15274–15278, 2009).