Update: Explain Why Networks Form with ERGM

Moving beyond intuition in explaining network formation

Are connections random, or driven by structure?
Do nodes with similar attributes connect more easily?
Do triangles actually shape how networks form?

Exponential Random Graph Models (ERGM) test these questions statistically.
They explain how ties form, not just what the network looks like.

With this update, ERGM no longer requires complex R coding.
You can now build and run ERGM with simple mouse clicks.

Follow the tutorial and test, directly, why your network forms the way it does.

※ NetMiner 5 ERGM is based on R and its packages.
To comply with the GPL, the functionality is not embedded directly in NetMiner.

What Can Statistical Network Analysis Reveal?

Network data are relational and inherently dependent.
NetMiner 5 helps you test network structure and explain how ties form.

Available on Windows and macOS with automatic updates.

Why does this network form the way it does?

ERGM statistically tests the structural and attribute drivers of network formation.
From model setup to results, everything runs without coding.

Do network measures differ across groups?

Group comparisons: t-Test, Mann–Whitney, ANOVA with permutation tests, Kruskal–Wallis
One-sample and paired tests: t-Test, Wilcoxon signed-rank

Are relationships connected to other variables or networks?

Do nodes with similar attributes connect more often?

What explains node attributes and relational patterns?

Node attributes explained by other variables: Regression analysis with permutation tests
Relational structure explained by other ties: Multiple network regression (MRQAP)

Image by Freepik

We’ve also fixed several known bugs.

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