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Network Community Detection Interactive Graph Model

Run the Network Community Detection Interactive Graph Model MicroSim Fullscreen

About This MicroSim

This graph illustrates how community-detection algorithms like Louvain partition a healthcare network into meaningful groups. The network contains patients, providers, diagnoses, and medications (distinguished by shape) that cluster into three disease cohorts — cardiac, diabetes, and renal — because connections within a cohort are dense while connections between cohorts are sparse. The few cross-cohort edges represent comorbid patients who bridge two communities.

How to Use

With "Color by community" on, each node is colored by the cohort the algorithm assigns it to; uncheck the box to see the same network without coloring, as the algorithm first sees it — the communities are not obvious until detected. Trace the sparse bridge edges (a patient with both diabetes and kidney disease) that link otherwise-separate cohorts. Drag nodes and use the navigation buttons to explore.

Iframe Embed Code

You can add this MicroSim to any web page by adding this to your HTML:

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<iframe src="https://dmccreary.github.io/modeling-healthcare-data/sims/network-community-detection-graph-model/main.html"
        height="450px"
        width="100%"
        scrolling="no"></iframe>

Lesson Plan

Grade Level

9-12 (High School Geometry)

Duration

10-15 minutes

Prerequisites

TODO: List prerequisites.

Activities

  1. Exploration (5 min): TODO
  2. Guided Practice (5 min): TODO
  3. Assessment (5 min): TODO

Assessment

TODO: List assessment criteria.

References

  1. TODO: Add references.