Alliance Network Analysis
Also known as: International Alliance Networks, Alliance Portfolio Network Analysis, Network Models of Alliance Formation, Interstate Alliance Graph Analysis
Alliance network analysis studies international alliances as a graph of states linked by formal security commitments, and models how that network forms and evolves. Rather than treating each alliance dyad as independent, it uses network science and inferential models such as the exponential random graph model (ERGM) — applied to alliance data by Cranmer, Desmarais, and Menninga (2012) — to capture the complex dependencies, such as a state's tendency to ally with its allies' allies, that ordinary dyadic regression assumes away.
Key highlights
- Models network dependence explicitly, correcting the independence violation that biases ordinary dyadic regression on relational data.
- Tests substantive structural hypotheses — transitivity, centralization, brokerage — directly as model parameters.
- Integrates endogenous network effects with exogenous nodal and dyadic covariates in a single coherent model.
- Connects IR to a mature toolkit of network science and inferential network modeling, including temporal and valued extensions.
Intuition
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How it works
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When to use it
Use alliance network analysis when alliance ties are interdependent and you want to explain the structure of the alliance system or test whether endogenous network processes — transitivity, preferential attachment, brokerage — shape who allies with whom, beyond state and dyad attributes. It is appropriate whenever ignoring network dependence would bias inference, which is the norm for relational IR data. It is less necessary when ties are genuinely independent, when the question is purely about exogenous dyadic predictors, or when the network is too small or too dense for stable ERGM estimation, where descriptive analysis or simpler models suffice.
Strengths & limitations
- Models network dependence explicitly, correcting the independence violation that biases ordinary dyadic regression on relational data.
- Tests substantive structural hypotheses — transitivity, centralization, brokerage — directly as model parameters.
- Integrates endogenous network effects with exogenous nodal and dyadic covariates in a single coherent model.
- Connects IR to a mature toolkit of network science and inferential network modeling, including temporal and valued extensions.
- ERGM estimation via MCMC can suffer model degeneracy, where the fitted model places nearly all probability on empty or complete graphs.
- Results are sensitive to which network statistics are specified; omitting a relevant dependence term biases the others.
- Large state systems and long time spans strain computation and may require temporal ERGM or separable temporal models.
- Alliance coding choices (defense pacts vs. ententes, formal vs. informal) materially change the network and thus the conclusions.
Common pitfalls
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Applications
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Frequently asked
Why not just use logistic regression on alliance dyads?
Because alliance ties are not independent: whether states i and j ally depends on their other ties (their shared partners, their positions as hubs or brokers). Ordinary logit on dyads assumes independence, which alliance data violate, biasing standard errors and missing exactly the structural processes of interest. ERGMs generalize logit to model the whole network, with the conditional form reducing to a logit for each tie given the rest of the graph.
What is model degeneracy in ERGMs and how is it handled?
Degeneracy occurs when a fitted ERGM concentrates almost all probability on degenerate graphs (empty or complete), so the model cannot reproduce a realistic network. It commonly arises with raw triangle counts. The standard remedy is to use geometrically weighted statistics — GWESP for transitivity, GWDEGREE for the degree distribution — which dampen the explosive feedback and make estimation stable.
How are changing alliances over time handled?
With temporal extensions. The temporal ERGM (TERGM) and the separable temporal ERGM (STERGM) model how the network at one time depends on its previous state, separating the formation and dissolution of ties. These let researchers study alliance persistence and change rather than treating each year as an independent snapshot.
Sources
- 1.Cranmer, S. J., Desmarais, B. A., & Menninga, E. J. (2012). Complex dependencies in the alliance network. Conflict Management and Peace Science, 29(3), 279–313.
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Cite this page
ScholarGate. (2026, June 22). Alliance Network Analysis. ScholarGate. https://scholargate.app/international-relations/alliance-network-analysis