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Simultaneous Community Detection and Missing Data Imputation for Networks with Node Covariates

Abstract Number:

3847 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Gauri Phatak (1), Katherine McLaughlin (1), James Molyneux (2)

Institutions:

(1) Oregon State University, N/A, (2) Swyfft, LLC, N/A

Co-Author(s):

Katherine McLaughlin  
Oregon State University
James Molyneux  
Swyfft, LLC

First Author:

Gauri Phatak  
Oregon State University

Presenting Author:

Gauri Phatak  
Oregon State University

Abstract Text:

It is challenging to perform analysis of social network data including community detection when there are missing values including node covariates, entire nodes, and edges. Node covariates provide an additional resource for network community detection in addition to the structure of the network. We propose an iterative method to simultaneously update missing covariates using imputation and perform covariate assisted community detection for networks modelled using Exponential Random Graph Models (ERGMs).
The proposed model is assessed using simulated network data with known communities and covariate values. In addition to simulated networks, time series of networks are generated based on human movement between Oregon cities that participated in a wastewater surveillance program run by a team at Oregon State University since mid-2020. The COVID wastewater data along with demographic and other COVID metrics are considered node covariates. Some of these covariates are assumed missing at random(MAR).Wastewater-based epidemiology is an effective approach to monitor the presence, prevalence, and trend of diseases, and understanding their spread through human movement networks.

Keywords:

Network community detection
|Wastewater based epidemiology
|Time series networks|Network Missing data imputation|Human movement network|Disease spread in human movement network

Sponsors:

Health Policy Statistics Section

Tracks:

Miscellaneous

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