Tight Query Complexity Bounds for Learning Graph Partitions

Abstract

Given a partition of a graph into connected components, the membership oracle asserts whether any two vertices of the graph lie in the same component or not. We prove that for $n\ge k\ge 2$, learning the components of an $n$-vertex hidden graph with $k$ components requires at least $(k-1)n-\binom k2$ membership queries. Our result improves on the best known information-theoretic bound of $\Omega(n\log k)$ queries, and exactly matches the query complexity of the algorithm introduced by [Reyzin and Srivastava, 2007] for this problem. Additionally, we introduce an oracle that can learn the number of components of $G$ in asymptotically fewer queries than learning the full partition, thus answering another question posed by the same authors. Lastly, we introduce a more applicable version of this oracle, and prove asymptotically tight bounds of $\widetilde\Theta(m)$ queries for both learning and verifying an $m$-edge hidden graph $G$ using it.

Cite

Text

Liu and Mukherjee. "Tight Query Complexity Bounds for Learning Graph Partitions." Conference on Learning Theory, 2022.

Markdown

[Liu and Mukherjee. "Tight Query Complexity Bounds for Learning Graph Partitions." Conference on Learning Theory, 2022.](https://mlanthology.org/colt/2022/liu2022colt-tight/)

BibTeX

@inproceedings{liu2022colt-tight,
  title     = {{Tight Query Complexity Bounds for Learning Graph Partitions}},
  author    = {Liu, Xizhi and Mukherjee, Sayan},
  booktitle = {Conference on Learning Theory},
  year      = {2022},
  pages     = {167-181},
  volume    = {178},
  url       = {https://mlanthology.org/colt/2022/liu2022colt-tight/}
}