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        <identifier>oai:drops-oai.dagstuhl.de:22673</identifier>
        <datestamp>2026-04-17T05:32:08Z</datestamp>
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          <dc:title>Distributed and Parallel Low-Diameter Decompositions for Arbitrary and Restricted Graphs</dc:title>
          <dc:creator>Dou, Jinfeng</dc:creator>
          <dc:creator>Götte, Thorsten</dc:creator>
          <dc:creator>Hillebrandt, Henning</dc:creator>
          <dc:creator>Scheideler, Christian</dc:creator>
          <dc:creator>Werthmann, Julian</dc:creator>
          <dc:subject>Distributed Graph Algorithms</dc:subject>
          <dc:subject>Network Decomposition</dc:subject>
          <dc:subject>Excluded Minor</dc:subject>
          <dc:description>We consider the distributed and parallel construction of low-diameter decompositions with strong diameter. We present algorithms for arbitrary undirected, weighted graphs and also for undirected, weighted graphs that can be separated through k ∈ Õ(1) shortest paths. This class of graphs includes planar graphs, graphs of bounded treewidth, and graphs that exclude a fixed minor K_r. Our algorithms work in the PRAM, CONGEST, and the novel HYBRID communication model and are competitive in all relevant parameters. Given 𝒟 &gt; 0, our low-diameter decomposition algorithm divides the graph into connected clusters of strong diameter 𝒟. For an arbitrary graph, an edge e ∈ E of length 𝓁_e is cut between two clusters with probability O(𝓁_e⋅log(n)/𝒟). If the graph can be separated by k ∈ Õ(1) paths, the probability improves to O(𝓁_e⋅log(log n)/𝒟). In either case, the decompositions can be computed in Õ(1) depth and Õ(m) work in the PRAM and Õ(1) time in the HYBRID model. In CONGEST, the runtimes are Õ(HD + √n) and Õ(HD) respectively. All these results hold w.h.p.&#13;
Broadly speaking, we present distributed and parallel implementations of sequential divide-and-conquer algorithms where we replace exact shortest paths with approximate shortest paths. In contrast to exact paths, these can be efficiently computed in the distributed and parallel setting [STOC '22]. Further, and perhaps more importantly, we show that instead of explicitly computing vertex-separators to enable efficient parallelization of these algorithms, it suffices to sample a few random paths of bounded length and the nodes close to them. Thereby, we do not require complex embeddings whose implementation is unknown in the distributed and parallel setting.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Jinfeng Dou and Thorsten Götte and Henning Hillebrandt and Christian Scheideler and Julian Werthmann</dc:contributor>
          <dc:date>2025</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 325, 16th Innovations in Theoretical Computer Science Conference (ITCS 2025)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ITCS.2025.45</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-226734</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2025.45</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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