AI & ML
Estimation and Recovery of a Planted Dense Subgraph from a Single Network Cascade
arXiv stat.TH0 views
arXiv:2610.08766v1 Announce Type: cross Abstract: We study the inference of a planted dense component in a sparse random graph from a single spreading process. The graph has an Erd\H{o}s-R\'enyi background with edge probability $p_n$ and contains a planted dense component of size $n^{\alpha}$, with $\alpha > 1/2$, whose internal edge density $\xi>0$ is constant. The edge set is unobserved; the data only consist of the successive infection times from a single realization of a continuous-time SI process with independent and exponentially distributed transmission times. We show that the planted dense component leaves a detectable signature in the spreading process: after the exploration enters the dense component, it undergoes a short phase of accelerated growth. By analyzing this phase, we localize its onset and endpoint. These localization results yield consistent estimators of the component-size exponent $\alpha$, the background edge density $p_n$, and the internal edge density $\xi$ from the infection times alone. When the identities of the infected vertices are also observed, we further establish consistent recovery of the planted dense component.
Source:https://arxiv.org/abs/2610.08766PreviousSharp detection thresholds for random geometric graphs with polynomial average degreesNextInformation geometry of L\'evy processes and financial models
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