Stat Cafe - Carson James

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Learning Joint and Individual Structure in Network Data with Covariates

Time: Monday 4/15/2024 from 11:40 AM to 12:30 PM
Location: BLOC 448
Pizza and drinks provided

Description

Datasets consisting of a network and covariates associated with its vertices have become ubiquitous. One problem pertaining to this type of data is to identify information unique to the network, information unique to the vertex covariates, and information that is shared between the network and the vertex covariates. This problem has been well studied in other contexts, such as for datasets consisting of multiple covariate matrices. Existing techniques for network data focus on capturing structure that is shared between a network and the vertex covariates but are not able to differentiate structure that is unique to each. This work formulates a solution to this problem via a low-rank model and a two-step estimation procedure, composed of an efficient spectral method to obtain an initial estimate for the joint structure followed by an optimization method that minimizes a nonconvex loss function associated with the model to obtain final estimates for the joint and individual structures.