Module 10 Activity Worksheet

Module: Module 10: Network Science and Graph Representation
Duration: 4 hours
Generated from the module page. Edit modules/module10.md, not this file.

Capability target

Build one connectome graph representation and justify two metric choices for a defined hypothesis.

You are done when you can demonstrate this, not when you have filled in every box below.

Before you start

Check that you have:

• Modules 01-09

Bring one question you already have about this topic. Write it here so you can check at the end whether it was answered:

My question:

Questions this module answers

Keep these in view. At the end, answer each in one sentence.

1. What information is lost or preserved by this graph abstraction?
   • Your answer:
2. Which metrics answer the biological question at hand?
   • Your answer:

The task

Scenario: You have the connectivity graph of 500 neurons in a cortical column from the MICrONS dataset. Your PI asks: “Is this circuit small-world? Are there hub neurons? Are there communities?”

1. Load the graph and compute basic statistics (nodes, edges, density, components).
2. Compute: degree distribution, clustering coefficient, average path length.
3. Compare to degree-preserving random graph and Watts-Strogatz small-world reference.
4. Identify candidate hub neurons (top 5% by degree or betweenness centrality).
5. Run community detection (Louvain or Leiden). Do detected communities align with cell types?
6. Write a 1-page graph analysis report with figures, metrics, null comparisons, and biological interpretation.

What you hand in

• Graph statistics summary table
• Degree distribution plot (log-log scale)
• Community detection results with cell-type comparison
• 1-page report

Working checklist

Tick as you go. If you skip a step, write why — a skipped step with a stated reason is a decision; a skipped step without one is a gap.

• Define node/edge schema: what are your nodes, what are your edges, what weighting scheme?
• Construct graph from synapse table (e.g., using CAVEclient + NetworkX). Inspect: number of nodes, edges, density, connected components.
• Compute candidate metrics: degree distribution, clustering, path length, reciprocity, modularity.
• Compare each metric to null-model expectation (degree-preserving random graph as minimum).
• Interpret metrics against hypothesis. Report which metrics are significant and which are not.
• Document abstraction limits: what information was lost in the graph construction?

Evidence and reasoning

Fill one row per claim you make in your artifact. A claim without a limitation is not finished.

Confidence. For your main claim, mark one and say why:

• High — two or more independent lines of evidence agree
• Medium — one strong line, or several that share a weakness
• Uncertain — the deciding evidence is not available to me

Why:

One alternative I considered and rejected, and the reason:

Misconception self-check

These are the errors this module is designed to prevent. Confirm you did not make them, or note where you nearly did:

• I did not assume: The connectivity graph is the data, rather than one lossy projection of it that discards all geometry.
• I did not assume: The synapse threshold is a technical detail that does not need reporting.
• I did not assume: A graph metric means the same thing biologically as it does in its original network-science context.
• I did not assume: Erdos-Renyi is an acceptable null for a spatially embedded, degree-heterogeneous connectome.

Session timing (facilitator reference)

Rubric

Score yourself before anyone else does. Where you fall short, name the specific next action rather than a general intention.

• Minimum pass
  • The node/edge schema is stated explicitly — node definition, edge direction, weighting, and synapse threshold — before any metric appears.
  • Each reported metric is paired with a null-model comparison; no bare metric values stand alone.
  • At least two metrics are linked in writing to the specific question they answer for the PI’s three asks (small-world, hubs, communities).
  • The report names at least one piece of information the graph abstraction discarded and one question it therefore cannot answer.
• Strong performance
  • The headline result is re-run at a second synapse threshold, and the report states which conclusions held and which moved.
  • Degree outliers are checked against the underlying reconstruction before being reported as hubs, with the check documented.
  • More than one null model is used, or the limits of the single null are stated in words (what it does and does not control for).
  • Detected communities are compared against external labels (cell types), and disagreement is reported as a finding rather than suppressed.
• Common failure to flag
  • Metric dumping — computing every available metric without explaining what question each answers.
  • Hub or community claims made without checking whether a merge error or threshold choice manufactured them.
  • A significance claim against Erdos-Renyi only, on a graph with obvious degree heterogeneity.

My self-assessment:

• Strongest part of my work, and the evidence for that:
• Weakest part, and the specific next action:

Exit prompt

State one reason a graph metric might be misleading in your current dataset.

Your answer:

Peer review (swap worksheets)

Reviewing someone else’s reasoning is the fastest way to see the gaps in your own. Assess the evidence quality, not whether you agree with the conclusion.

• Is every claim paired with specific evidence?
• Is at least one limitation stated, and is it a real one?
• Is the confidence level justified by the number of independent evidence lines?
• One thing this person did better than me:
• One question I would ask them:

Module page: /modules/module10/ · Slides: /modules/slides/module10/ · Facilitator guide