Project Two-Mode Networks Responsibly
Convert affiliations to actor connections only after choosing a weighting rule and showing how popular events can dominate the projection.
Method sources
By the end of this tutorial
- 1Define one-mode projection of bipartite data from an explicit node, tie, boundary, direction, weight, and observation-window specification.
- 2Apply and audit this procedure: Generate raw co-attendance weights, repeat with inverse event-size or another justified weighting, compare density and rankings, and retain the bipartite view beside every projected figure.
- 3Interpret the result with a sensitivity check and the following evidence boundary: Projection creates inferred actor ties and dense cliques around popular events; it does not show direct interaction, collaboration quality, or equal exposure.
SNA
Network specification
Analysis scenario
An academic-development office wants a staff collaboration map from attendance at 40 workshops, including several very large orientation events.
Nodes
Staff members in the actor projection, derived from a separate bipartite roster of staff and eligible workshops.
Ties
A weighted staff-to-staff link based on shared workshop attendance under a documented projection and event-size weighting rule.
Network type
Undirected weighted one-mode projection accompanied by its original binary staff-by-workshop bipartite matrix.
Step-by-step tutorial
Freeze the relational question
Write the decision the analysis must inform, then lock the eligible node roster, tie-generating event, direction, weight, self-tie rule, observation window, and missing-data code. Preserve a read-only source copy and record why this specification represents the stated question.
Checkpoint
A second analyst can reconstruct the same node set and edge table from the written rules without guessing what an absent record means.
Compute the focal structure
Work on a versioned analysis copy and carry out the focal method exactly as specified: Generate raw co-attendance weights, repeat with inverse event-size or another justified weighting, compare density and rankings, and retain the bipartite view beside every projected figure. Save software and package versions, every threshold, normalization, seed, and intermediate count needed to reproduce the result.
Checkpoint
The output is tied to one named data version and includes the denominator, parameter settings, and a reproducible calculation record.
Run a structural sensitivity check
Repeat the analysis under at least one defensible alternative boundary, missingness rule, tie threshold, weight transformation, or model setting. Compare membership and substantive conclusions, not only a single coefficient, and investigate every change large enough to alter a decision.
Checkpoint
The audit states which patterns persist, which actors or groups change classification, and which conclusion depends on an analyst choice.
Report for responsible action
Pair the numerical result with a table or structure-preserving visual, document excluded and missing actors, and explain uncertainty in plain language. Convert the finding into a reversible support question, not an automatic ranking, while stating this boundary: Projection creates inferred actor ties and dense cliques around popular events; it does not show direct interaction, collaboration quality, or equal exposure.
Checkpoint
The final note contains the question, specification, result, sensitivity evidence, uncertainty, privacy controls, and a proportionate next step.
Interpret with care
one-mode projection of bipartite data describes a property of the specified relation and network boundary. It does not transfer automatically to another relation, time period, class, platform, or population.
Projection creates inferred actor ties and dense cliques around popular events; it does not show direct interaction, collaboration quality, or equal exposure. Compare the result with raw counts, missingness, plausible alternative specifications, and contextual evidence before acting.