Refined Vertex Sparsifiers of Planar Graphs
February 20, 2017 Β· Declared Dead Β· π SIAM Journal on Discrete Mathematics
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Authors
Robert Krauthgamer, Havana, Rika
arXiv ID
1702.05951
Category
cs.DS: Data Structures & Algorithms
Citations
31
Venue
SIAM Journal on Discrete Mathematics
Last Checked
3 months ago
Abstract
We study the following version of cut sparsification. Given a large edge-weighted network $G$ with $k$ terminal vertices, compress it into a smaller network $H$ with the same terminals, such that every minimum terminal cut in $H$ approximates the corresponding one in $G$, up to a factor $q\geq 1$ that is called the quality. (The case $q=1$ is known also as a mimicking network). We provide new insights about the structure of minimum terminal cuts, leading to new results for cut sparsifiers of planar graphs. Our first contribution identifies a subset of the minimum terminal cuts, which we call elementary, that generates all the others. Consequently, $H$ is a cut sparsifier if and only if it preserves all the elementary terminal cuts (up to this factor $q$). This structural characterization lead to improved bounds on the size of $H$. For example, it improve the bound of mimicking-network size for planar graphs into a near-optimal one. Our second and main contribution is to refine the known bounds in terms of $Ξ³=Ξ³(G)$, which is defined as the minimum number of faces that are incident to all the terminals in a planar graph $G$. We prove that the number of elementary terminal cuts is $O((2k/Ξ³)^{2Ξ³})$ (compared to $O(2^k)$ terminal cuts), and furthermore obtain a mimicking-network of size $O(Ξ³2^{2Ξ³} k^4)$, which is near-optimal as a function of $Ξ³$. In the analysis we break the elementary terminal cuts into fragments, and count them carefully. Our third contribution is a duality between cut sparsification and distance sparsification for certain planar graphs, when the sparsifier $H$ is required to be a minor of $G$. This duality connects problems that were previously studied separately, implying new results, new proofs of known results, and equivalences between open gaps.
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