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Social Scripts02:10

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People tend to know what behavior is expected of them in specific, familiar settings. A script is a person’s knowledge about the sequence of events expected in a specific setting (Schank & Abelson, 1977). Essentially, scripts are a particular kind of schema, one containing default values for the features within an event. In the restaurant example, the script's features include the props (e.g., tables, menu, food, and money), the roles to be played (e.g., customer and waiter),...
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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Social Network Spatial Model.

Joseph T Ciminelli1, Tanzy Love2, Tong Tong Wu2

  • 1University of Rochester, 500 Joseph Wilson Boulevard, RC Box 270138, Rochester, NY 14623.

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|August 29, 2019
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Summary
This summary is machine-generated.

This study introduces a new spatial modeling method integrating social network data. It models attributes as a continuous process in social space, enhancing traditional spatial frameworks.

Keywords:
node attributessocial networksocial space

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Area of Science:

  • Spatial statistics
  • Network analysis
  • Computational social science

Background:

  • Traditional spatial models often overlook social network structures.
  • Social network data offers valuable insights into correlations and attribute distributions.
  • Integrating network data can enhance the accuracy and scope of spatial analyses.

Purpose of the Study:

  • To develop a novel method for modeling spatial correlations within social networks.
  • To integrate social network data into existing spatial modeling frameworks.
  • To simultaneously model unobserved social space locations and spatial processes.

Main Methods:

  • Modeling attributes as a continuous spatial process over social connections.
  • Simultaneously estimating network member locations and spatial processes.
  • Utilizing observed network connections and nodal attributes for model inference.

Main Results:

  • The proposed method effectively incorporates social network data into spatial models.
  • Simulation studies validated the model's performance.
  • The approach was successfully applied to real-world networks (emergency response, teenage physical activity).

Conclusions:

  • The introduced methods expand traditional spatial models by incorporating network data.
  • This approach provides a robust framework for analyzing spatially correlated attributes in social networks.
  • The findings have implications for understanding complex systems where social connections influence spatial patterns.