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        <datestamp>2024-03-06T10:40:18Z</datestamp>
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          <dc:title>Efficient Construction of Probabilistic Tree Embeddings</dc:title>
          <dc:creator>Blelloch, Guy E.</dc:creator>
          <dc:creator>Gu, Yan</dc:creator>
          <dc:creator>Sun, Yihan</dc:creator>
          <dc:subject>Graph Algorithm</dc:subject>
          <dc:subject>Metric Embeddings</dc:subject>
          <dc:subject>Probabilistic Tree Embeddings</dc:subject>
          <dc:subject>Single-source Shortest-paths</dc:subject>
          <dc:description>In this paper we describe an algorithm that embeds a graph metric (V,d_G) on an undirected weighted graph G=(V,E) into a distribution of tree metrics (T,D_T) such that for every pair u,v in V, d_G(u,v)&lt;=d_T(u,v) and E_T[d_T(u,v)]&lt;=O(log n)d_G(u,v).  Such embeddings have proved highly useful in designing fast approximation algorithms, as many hard problems on graphs are easy to solve on tree instances.  For a graph with n vertices and m edges, our algorithm runs in O(m log n) time with high probability, which improves the previous upper bound of O(m log^3 n) shown by Mendel et al. in 2009.&#13;
&#13;
The key component of our algorithm is a new approximate single-source shortest-path algorithm, which implements the priority queue with a new data structure, the bucket-tree structure. The algorithm has three properties: it only requires linear time in terms of the number of edges in the input graph; the computed distances have the distance preserving property; and when computing the shortest-paths to the k-nearest vertices from the source, it only requires to visit these vertices and their edge lists. These properties are essential to guarantee the correctness and the stated work bound.&#13;
&#13;
Using this shortest-path algorithm, we show how to generate an intermediate structure, the approximate dominance sequences of the input graph, in O(m log n) time, and further propose a simple yet efficient algorithm to converted this sequence to a tree embedding in O(n log n) time, both with high probability. Combining the three subroutines gives the stated work bound of the algorithm. &#13;
&#13;
We also show a new application of probabilistic tree embeddings: they can be used to accelerate the construction of a series of approximate distance oracles.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Guy E. Blelloch and Yan Gu and Yihan Sun</dc:contributor>
          <dc:date>2017</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 80, 44th International Colloquium on Automata, Languages, and Programming (ICALP 2017)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ICALP.2017.26</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-75034</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2017.26</dc:identifier>
          <dc:language>eng</dc:language>
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