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Activity Number: 162 - SPEED: Government Statistics, Health Policy, and Marketing
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Defense and National Security
Abstract #323587
Title: A Community and Node Attribute-Corrected Stochastic Blockmodel
Author(s): Kristen M. Altenburger and W. Philip Kegelmeyer and Ali Pinar and Jeremy D. Wendt and Cliff Anderson-Bergman*
Companies: Stanford University and Sandia National Laboratories and Sandia National Laboratories and Sandia National Laboratorie and Sandia National Laboratories
Keywords: Community detection ; Node attributes ; Network models

Community structure in a network is commonly influenced by homophily, the phenomenon that interactions are more likely to occur between nodes with similar attributes. Yet, generative models of community structure based only on the network topology may miss community structure determined by node attribute relations, while models that strictly enforce homophilous communities will be too restrictive. To model communities that may exhibit homophily in observed networks, we propose a generative model, the "Community and Node Attribute-Corrected Stochastic Blockmodel" (canacSBM), which preserves the joint structure of observed node attribute interactions and estimated community structure. We first introduce a flexible non-parametric method for inferring community structure, resulting in a data object we call the "stable structure matrix". Then, conditional on the estimated communities, we allow heterogeneity in community-level attribute dependencies. Finally, we apply canacSBM to a real-world network that represents a national security application and observe how the model appropriately recognizes the joint node attribute and community structure.

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

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