Inferring social structure from continuous-time interaction data

被引:0
作者
Lee, Wesley [1 ]
Fosdick, Bailey [2 ]
McCormick, Tyler [1 ]
机构
[1] Univ Washington, Seattle, WA 98195 USA
[2] Colorado State Univ, Ft Collins, CO 80523 USA
基金
美国国家科学基金会;
关键词
continuous time network; latent network; point process; relational event data; LATENT SPACE MODELS; TAIL-STREAMERS; NETWORKS;
D O I
10.1002/asmb.2285
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
Relational event data, which consist of events involving pairs of actors over time, are now commonly available at the finest of temporal resolutions. Existing continuous-time methods for modeling such data are based on point processes and directly model interaction contagion, whereby one interaction increases the propensity of future interactions among actors, often as dictated by some latent variable structure. In this article, we present an alternative approach to using temporal-relational point process models for continuous-time event data. We characterize interactions between a pair of actors as either spurious or as resulting from an underlying, persistent connection in a latent social network. We argue that consistent deviations from expected behavior, rather than solely high frequency counts, are crucial for identifying well-established underlying social relationships. This study aims to explore these latent network structures in two contexts: one comprising of college students and another involving barn swallows.
引用
收藏
页码:87 / 104
页数:18
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