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Network data are 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.

Table of Contents

  • Chapter One

    Introduction

  • Chapter Two

    Aims

  • Chapter Three

    Graph Neural Networks

  • Chapter Four

    Policy-Relevant Network Data

  • Chapter Five

    Predictive Modeling Over Networks

  • Chapter Six

    Generative Modeling Over Networks

  • Chapter Seven

    Exponential Random Graph Models

  • Chapter Eight

    Network Fusion

  • Chapter Nine

    Outlook

  • Appendix A

    Methodological Needs in Public Policy Network Science

  • Appendix B

    Mathematical Glossary

  • Appendix C

    The NDSSL Sociocentric Dataset

  • Appendix D

    The FluPaths Egocentric Dataset

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The research described in this report was conducted by RAND Health Care.

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