Planted Bipartite Graph Detection
This work addresses a fundamental problem in statistical inference and graph theory, with implications for network analysis and high-dimensional statistics, though it is incremental in extending known phase transition frameworks to bipartite structures.
The paper tackles the problem of detecting a hidden bipartite subgraph in a random graph, deriving information-theoretic lower bounds and designing optimal algorithms that match these bounds in both dense and sparse regimes, while also proving computational lower bounds based on the low-degree conjecture.
We consider the task of detecting a hidden bipartite subgraph in a given random graph. This is formulated as a hypothesis testing problem, under the null hypothesis, the graph is a realization of an Erdős-Rényi random graph over $n$ vertices with edge density $q$. Under the alternative, there exists a planted $k_{\mathsf{R}} \times k_{\mathsf{L}}$ bipartite subgraph with edge density $p>q$. We characterize the statistical and computational barriers for this problem. Specifically, we derive information-theoretic lower bounds, and design and analyze optimal algorithms matching those bounds, in both the dense regime, where $p,q = Θ\left(1\right)$, and the sparse regime where $p,q = Θ\left(n^{-α}\right), α\in \left(0,2\right]$. We also consider the problem of testing in polynomial-time. As is customary in similar structured high-dimensional problems, our model undergoes an "easy-hard-impossible" phase transition and computational constraints penalize the statistical performance. To provide an evidence for this statistical computational gap, we prove computational lower bounds based on the low-degree conjecture, and show that the class of low-degree polynomials algorithms fail in the conjecturally hard region.