alterlab-sna

Social Network Analysis — Match the Measure to the Question

Skill type: ANALYSIS MODULE. SNA treats the ties as the data. The discipline is not running a centrality function — it is choosing the centrality that answers the substantive question, respecting network dependence (ties are not independent observations), and using an inferential model (ERGM) when you want to explain tie formation rather than describe it. Computation is handed to alterlab-networkx.

Core Mission

THE TIE IS THE UNIT. PICK THE CENTRALITY THAT MATCHES THE QUESTION; TIES ARE NOT IID.

When to Use This Skill

• "Run a social network analysis / compute centrality on my network."
• "Who are the key players / brokers in this network?"
• "Detect communities / clusters in my network."
• "Fit an ERGM to explain why ties form."

Does NOT Trigger

The request is really about…	Route to	Why not this skill
General graph algorithms / plotting / I/O	alterlab-networkx	This skill adds SNA method choice; networkx executes.
Graph neural networks / node embeddings for prediction	alterlab-torch-geometric	Deep learning on graphs, not SNA description/inference.
Whether a relational design is appropriate	alterlab-ssci-design-gate	Design routing, upstream.
Plain tabular statistics	alterlab-statistical-analysis	No network structure.

Centrality — pick by the substantive claim (verified networkx 3.x)

Question	Centrality	Call
Who has the most direct ties?	degree	nx.degree_centrality(G)
Who bridges otherwise-separate groups?	betweenness (brokerage)	nx.betweenness_centrality(G)
Who can reach everyone fastest?	closeness	nx.closeness_centrality(G)
Who is connected to well-connected others?	eigenvector / PageRank	nx.eigenvector_centrality(G) / nx.pagerank(G)

Do not report "centrality" generically — name which one and why it matches the claim (a broker argument needs betweenness, not degree).

Community detection (verified)

• nx.community.louvain_communities(G) — native in networkx 3.x (no python-louvain needed).
• nx.community.greedy_modularity_communities(G) — Clauset-Newman-Moore.
• nx.community.modularity(G, communities) — score a partition.
• Leiden (Traag et al.) guarantees well-connected communities and is the recommended improvement over Louvain. networkx ≥ 3.7 ships it natively: nx.community.leiden_communities(G, metric="modularity", seed=...) (on 3.5–3.6 the function exists but is backend-only). For large graphs use igraph g.community_leiden(objective_function="modularity") or leidenalg. Both networkx and igraph default to the CPM objective, which at resolution 1 can return near-singleton partitions — pass the modularity objective (or a deliberate CPM resolution) explicitly.

Inference: ERGM (no mature Python — use R statnet)

To explain tie formation (reciprocity, homophily, triadic closure) rather than describe it, use an exponential random graph model. There is no mature Python ERGM package (pyERGM exists but is young); the standard is R statnet/ergm via an R bridge:

library(ergm)
model <- ergm(net ~ edges + mutual + nodematch("group") + gwesp(0.5, fixed = TRUE))
summary(model)

Declare the R dependency; do not fabricate a Python ERGM API. Full algorithm-selection guidance, the ERGM term glossary, and the dependence caveat: references/sna_methods.md.

The dependence caveat

Network ties violate the independence assumption of ordinary regression: you cannot regress an outcome on centrality and read the SE naively, because nodes are linked. Use permutation/QAP tests for dyadic hypotheses, or ERGM/latent-space models for tie-level inference. Flag this whenever a network measure is fed into a downstream test.

Output Template

NETWORK:      <directed? weighted? n nodes, m edges; how ties were defined>
MEASURE:      <named centrality/community algorithm + why it matches the question>
RESULT:       <top nodes / partition + modularity; not just a number>
INFERENCE:    <ERGM terms + fit, OR permutation/QAP test> — respect tie dependence
CLAIM SCOPE:  descriptive (structure) vs inferential (tie-formation) — do not conflate

References

• references/sna_methods.md — centrality selection, Louvain vs Leiden, ERGM terms + R bridge, QAP/permutation, large-graph tooling.

Part of the AlterLab Academic Skills suite.