[edit]

# Planting trees in graphs, and finding them back

*Proceedings of the Thirty-Second Conference on Learning Theory*, PMLR 99:2341-2371, 2019.

#### Abstract

In this paper we study the two inference problems of detection and reconstruction in the context of planted structures in sparse Erdős-Rényi random graphs $\mathcal G(n,\lambda/n)$ with fixed average degree $\lambda>0$. Motivated by a problem of communication security, we focus on the case where the planted structure consists in the addition of a tree graph. In the case of planted line graphs, we establish the following phase diagram for detection and reconstruction. In a low density region where the average degree $\lambda$ of the original graph is below some critical value $\lambda_c=1$, both detection and reconstruction go from impossible to easy as the line length $K$ crosses some critical value $K^*=\ln(n)/\ln(1/\lambda)$, where $n$ is the number of nodes in the graph. In a high density region where $\lambda>\lambda_c$, detection goes from impossible to easy as $K$ goes from $o(\sqrt{n})$ to $\omega(\sqrt{n})$. In contrast, reconstruction remains impossible so long as $K=o(n)$. We then consider planted $D$-ary trees of varying depth $h$ and $2\le D\le O(1)$. For these we identify a low-density region $\lambda<\lambda_D$, where $\lambda_D$ is the threshold for emergence of the $D$-core in Erdős-Rényi random graphs $\mathcal G(n,\lambda/n)$ for which the following holds. There is a threshold $h*=g(D)\ln(\ln(n))$ with the following properties. Detection goes from impossible to feasible as $h$ crosses $h*$. Interestingly, we show that only partial reconstruction is feasible at best for $h\ge h*$. We conjecture a similar picture to hold for $D$-ary trees as for lines in the high-density region $\lambda>\lambda_D$, but confirm only the following part of this picture: Detection is easy for $D$-ary trees of size $\omega(\sqrt{n})$, while at best only partial reconstruction is feasible for $D$-ary trees of any size $o(n)$. These results provide a clear contrast with the corresponding picture for detection and reconstruction of {\em low rank} planted structures, such as dense subgraphs and block communities. In the examples we study, there is i) an absence of hard phases for both detection and reconstruction, and ii) a discrepancy between detection and reconstruction, the latter being impossible for a wide range of parameters where detection is easy. The latter property does not hold for previously studied low rank planted structures.