English

Maintaining Expander Decompositions via Sparse Cuts

Data Structures and Algorithms 2023-01-24 v3

Abstract

In this article, we show that the algorithm of maintaining expander decompositions in graphs undergoing edge deletions directly by removing sparse cuts repeatedly can be made efficient. Formally, for an mm-edge undirected graph GG, we say a cut (S,S)(S, \overline{S}) is ϕ\phi-sparse if EG(S,S)<ϕmin{volG(S),volG(S)}|E_G(S, \overline{S})| < \phi \cdot \min\{vol_G(S), vol_G(\overline{S})\}. A ϕ\phi-expander decomposition of GG is a partition of VV into sets X1,X2,,XkX_1, X_2, \ldots, X_k such that each cluster G[Xi]G[X_i] contains no ϕ\phi-sparse cut (meaning it is a ϕ\phi-expander) with O~(ϕm)\tilde{O}(\phi m) edges crossing between clusters. A natural way to compute a ϕ\phi-expander decomposition is to decompose clusters by ϕ\phi-sparse cuts until no such cut is contained in any cluster. We show that even in graphs undergoing edge deletions, a slight relaxation of this meta-algorithm can be implemented efficiently with amortized update time mo(1)/ϕ2m^{o(1)}/\phi^2. Our approach naturally extends to maintaining directed ϕ\phi-expander decompositions and ϕ\phi-expander hierarchies and thus gives a unifying framework while having simpler proofs than previous state-of-the-art work. In all settings, our algorithm matches the run-times of previous algorithms up to subpolynomial factors. Moreover, our algorithm provides stronger guarantees for ϕ\phi-expander decompositions. For example, for graphs undergoing edge deletions, our approach is the first to maintain a dynamic expander decomposition where each updated decomposition is a refinement of the previous decomposition, and our approach is the first to guarantee a sublinear ϕm1+o(1)\phi m^{1+o(1)} bound on the total number of edges that cross between clusters across the entire sequence of dynamic updates.

Keywords

Cite

@article{arxiv.2204.02519,
  title  = {Maintaining Expander Decompositions via Sparse Cuts},
  author = {Yiding Hua and Rasmus Kyng and Maximilian Probst Gutenberg and Zihang Wu},
  journal= {arXiv preprint arXiv:2204.02519},
  year   = {2023}
}
R2 v1 2026-06-24T10:39:12.293Z