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        <datestamp>2024-03-06T10:36:18Z</datestamp>
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          <dc:title>Tight Bounds for Graph Problems in Insertion Streams</dc:title>
          <dc:creator>Sun, Xiaoming</dc:creator>
          <dc:creator>Woodruff, David P.</dc:creator>
          <dc:subject>communication complexity</dc:subject>
          <dc:subject>data streams</dc:subject>
          <dc:subject>graphs</dc:subject>
          <dc:subject>space complexity</dc:subject>
          <dc:description>Despite the large amount of work on solving graph problems in the data stream model, there do not exist tight space bounds for almost any of them, even in a stream with only edge insertions. For example, for testing connectivity, the upper bound is O(n * log(n)) bits, while the lower bound is only Omega(n) bits. We remedy this situation by providing the first tight Omega(n * log(n)) space lower bounds for randomized algorithms which succeed with constant probability in a stream of edge insertions for a number of graph problems. Our lower bounds apply to testing bipartiteness, connectivity, cycle-freeness, whether a graph is Eulerian, planarity, H-minor freeness, finding a minimum spanning tree of a connected graph, and testing if the diameter of a sparse graph is constant. We also give the first Omega(n * k * log(n)) space lower bounds for deterministic algorithms for k-edge connectivity and k-vertex connectivity; these are optimal in light of known deterministic upper bounds (for k-vertex connectivity we also need to allow edge duplications, which known upper bounds allow). Finally, we give an Omega(n * log^2(n)) lower bound for randomized algorithms approximating the minimum cut up to a constant factor with constant probability in a graph with integer weights between 1 and n, presented as a stream of insertions and deletions to its edges. This lower bound also holds for cut sparsifiers, and gives the first separation of maintaining a sparsifier in the data stream model versus the offline model.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Xiaoming Sun and David P. Woodruff</dc:contributor>
          <dc:date>2015</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 40, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2015)</dc:relation>
          <dc:type>InProceedings</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.APPROX-RANDOM.2015.435</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-53160</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2015.435</dc:identifier>
          <dc:language>eng</dc:language>
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