Information-Theoretic Thresholds for Planted Dense Cycles
Cheng Mao, Alexander S. Wein, Shenduo Zhang · IEEE Transactions on Information Theory · 2024
We study a random graph model for small-world networks which are ubiquitous in social and biological sciences. In this model, a dense cycle of expected bandwidth$n \tau $, representing the hidden one-dimensional geometry of vertices, is planted in an ambient random graph on n vertices. For both detection and recovery of the planted dense cycle, we characterize the information-theoretic thresholds in terms of n,$\tau $, and an edge-wise signal-to-noise ratio$\lambda $. In particular, the information-theoretic thresholds differ from the computational thresholds established in a recent work for low-degree polynomial algorithms, thereby justifying the existence of statistical-to-computational gaps for this problem.