Estimating homophily in social networks using dyadic predictions

• Main Authors: BERRY, George, SIRIANNI, Antonio, WEBER, Ingmar, AN, Jisun, MACY, Michael
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: homophily; networks; machine learning; quantitative methodology; Digital Communications and Networking; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/6225; https://ink.library.smu.edu.sg/context/sis_research/article/7228/viewcontent/SocSci_v8_285to307.pdf
• Institution: Singapore Management University

Predictions of node categories are commonly used to estimate homophily and other relational properties in networks. However, little is known about the validity of using predictions for this task. We show that estimating homophily in a network is a problem of predicting categories of dyads (edges) in...