Hub Neighbor-Degree Diagnostics for Sparse Random Graphs

Networks with nearly identical degree distributions can place their hubs in sharply different neighborhoods.

We develop a model diagnostic based on the mean degree of the neighbors of a degree-$k$ vertex.

Under rank-one inhomogeneous random graphs, this statistic has degree-invariant centering and $k^{-1/2}$ fluctuations.

Under non-rank-one kernels, posterior uncertainty about the root type can instead determine both centering and scale.

Under linear preferential attachment, the statistic grows as $(m+\delta)\log k$.

We turn these model-specific limits into goodness-of-fit tests for specified sparse-graph nulls and a weighted log-degree slope test for residual hub-neighborhood trends.

Simulations evaluate null calibration, degree-distribution misspecification, and power against degree-matched preferential-attachment alternatives.

Applications to high-school contact and arXiv coauthorship networks show that the method separates level misspecification from disassortative and positive residual trends.

Reddit interaction networks provide a further appendix example.