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Graph Algorithm Family Map

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Description

Select any family or algorithm for its definition. This chapter’s tour collapses the map to the root and four families; selecting a family reveals its leaves. Show all algorithms restores the full taxonomy. Each diagram node supports Tab followed by Enter or Space.

Lesson Plan

Audience: undergraduate students and healthcare data practitioners. Allow 10–15 minutes. Prerequisites: nodes, edges, labels, and properties; for the comparison, also primary and foreign keys.

Learning objective: Classify a graph algorithm by the type of question it answers (distance, importance, grouping, or prediction) and summarize one example algorithm per family.

  1. Explore: Use the tour to classify three questions: find a short referral path, identify a provider bridging referral groups, and find disconnected provider groups. Open the relevant families and compare their algorithms.
  2. Explain: Summarize one algorithm per family. Explain why clustering coefficient measures local cohesion rather than returning a partition, and why a similarity score is not automatically a calibrated probability.
  3. Transfer: Sketch a different healthcare example using the same concept and explain one modeling assumption.

Assessment: use the explanation prompt above as an exit ticket. A complete response names the relevant graph elements, traces the displayed evidence, and distinguishes a modeling assumption from a general claim.

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Copy this iframe to your website:

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Quality Checks

Validated September 7, 2026. Completeness rubric: 100/100. Browser interaction tests passed at 400, 800, and 1200 px, including reset and keyboard controls; no JavaScript errors were observed. Iframe visibility and visual layout checks passed. The iframe resizes to its rendered content, with the declared height serving as a fallback.

Specification

The full specification below is extracted from Chapter 5: Graph Algorithms, Centrality, and Similarity Measures.

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Type: diagram
**sim-id:** graph-algorithm-family-map<br/>
**Library:** Mermaid<br/>
**Status:** Validated

Bloom Taxonomy: Understand<br/>
Bloom Taxonomy Verb: classify, summarize<br/>
Learning objective: Classify a graph algorithm by the type of question it answers (distance, importance, grouping, or prediction) and summarize one example algorithm per family.

Purpose: Give learners a mental map of this chapter before diving into individual algorithms, so each new algorithm they meet has an obvious "home" in the taxonomy.

Structure: A Mermaid flowchart with a root node "Graph Algorithm" branching into four family nodes: "Distance Questions", "Importance Questions", "Grouping Questions", "Prediction Questions". Each family node branches to 2-3 example algorithm leaf nodes:

- Distance Questions → Shortest Path Algorithm
- Importance Questions → Degree Centrality, Betweenness Centrality, PageRank Algorithm
- Grouping Questions → Connected Components, Strongly Connected Component, Clustering Coefficient
- Prediction Questions → Similarity Measure, Link Prediction

Every node must have a `click` directive that opens an infobox with a one-sentence plain-language definition of that family or algorithm (e.g., clicking "Importance Questions" shows "Which nodes matter most, and by what definition of 'matters'?"; clicking "Degree Centrality" shows its formal one-line definition).

Color scheme: root node gray, family nodes blue, leaf nodes colored by family (distance=green, importance=orange, grouping=purple, prediction=teal)

Interactivity requirement: every node clickable with an infobox; the diagram title bar includes a "this chapter's tour" reset button that collapses back to the four family nodes.

Implementation: Mermaid flowchart with `click NodeId call showInfo("id")` directives wired to a small JavaScript infobox panel below the diagram, responsive to window resize.

References