Hierarchical structure can improve prediction of missing network connections.

Many networks contain nested groups: communities divide into smaller communities, and functional units sit inside broader systems. A hierarchical random-graph model estimates connection probabilities from the shared position of two nodes in this inferred hierarchy. Unconnected pairs with high average probability become candidates for missing links. This can outperform methods focused only on immediate neighbours because it captures mesoscale organisation, not just the geometry of short paths.