Deep Generative Modeling in Network Science with Applications to Public Policy Research

• URL: http://arxiv.org/abs/2010.07870v2
• Date: Fri, 16 Oct 2020 23:31:09 GMT
• Title: Deep Generative Modeling in Network Science with Applications to Public Policy Research
• Authors: Gavin S. Hartnett, Raffaele Vardavas, Lawrence Baker, Michael Chaykowsky, C. Ben Gibson, Federico Girosi, David P. Kennedy, Osonde A. Osoba
• Abstract summary: Network data is increasingly being used in quantitative, data-driven public policy research. Deep generative methods can be used to generate realistic synthetic networks useful for microsimulation and agent-based models. We develop a new generative framework which applies to large social contact networks commonly used in epidemiological modeling.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network data is increasingly being used in quantitative, data-driven public policy research. These are typically very rich datasets that contain complex correlations and inter-dependencies. This richness both promises to be quite useful for policy research, while at the same time posing a challenge for the useful extraction of information from these datasets - a challenge which calls for new data analysis methods. In this report, we formulate a research agenda of key methodological problems whose solutions would enable new advances across many areas of policy research. We then review recent advances in applying deep learning to network data, and show how these methods may be used to address many of the methodological problems we identified. We particularly emphasize deep generative methods, which can be used to generate realistic synthetic networks useful for microsimulation and agent-based models capable of informing key public policy questions. We extend these recent advances by developing a new generative framework which applies to large social contact networks commonly used in epidemiological modeling. For context, we also compare and contrast these recent neural network-based approaches with the more traditional Exponential Random Graph Models. Lastly, we discuss some open problems where more progress is needed.

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