Localized geometry detection in scale-free random graphs
Gianmarco Bet, Riccardo Michielan, Clara Stegehuis · Journal of Applied Probability · 2025
Abstract We consider the problem of detecting whether a power-law inhomogeneous random graph contains a geometric community, and we frame this as a hypothesis-testing problem. More precisely, we assume that we are given a sample from an unknown distribution on the space of graphs on n vertices. Under the null hypothesis, the sample originates from the inhomogeneous random graph with a heavy-tailed degree sequence. Under the alternative hypothesis, k equals o left parenthesis n right parenthesis k = o ( n ) $k=o(n)$ vertices are given spatial locations and connect following the geometric inhomogeneous random graph connection rule. The remaining n minus k n − k $n-k$ vertices follow the inhomogeneous random graph connection rule. We propose a simple and efficient test based on counting normalized triangles to differentiate between the two hypotheses. We prove that our test correctly detects the presence of the community with high probability as n right arrow normal infinity n → ∞ $n\to\infty$ , and identifies large-degree vertices of the community with high probability.