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Activity Number: 69 - Network Analysis
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313143
Title: Bias-Variance Tradeoffs in Joint Spectral Embeddings
Author(s): Benjamin Draves* and Daniel L Sussman
Companies: Boston University and Boston University
Keywords: Network Analysis ; Central Limit Theorem; Network Hypothesis Test; Community Detection

Multiple network analysis has applications in a multitude of research settings including social network analysis, dynamical biological networks, and connectomics. Joint embedding techniques aim to represent these networks in a common, low dimensional space by mapping the vertices of the networks to points in Euclidean space. This representation enables researchers to employ statistical and machine learning techniques to address research problems in multiple network analysis. We study one such embedding, the Omnibus embedding, and prove a Central Limit Theorem which reveals a bias-variance tradeoff introduced when analyzing networks with different connectivity structure. We study the ramifications of this bias-variance tradeoff in subsequent statistical tasks including estimation performance, multiplex community detection, and network hypothesis testing. Finally, we provide simulation studies that verify our asymptotic theory in finite sample networks and demonstrate how this bias-variance tradeoff enables robust multiple network analysis.

Authors who are presenting talks have a * after their name.

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