21 research outputs found

    A simple and optimal ancestry labeling scheme for trees

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    We present a lgn+2lglgn+3\lg n + 2 \lg \lg n+3 ancestry labeling scheme for trees. The problem was first presented by Kannan et al. [STOC 88'] along with a simple 2lgn2 \lg n solution. Motivated by applications to XML files, the label size was improved incrementally over the course of more than 20 years by a series of papers. The last, due to Fraigniaud and Korman [STOC 10'], presented an asymptotically optimal lgn+4lglgn+O(1)\lg n + 4 \lg \lg n+O(1) labeling scheme using non-trivial tree-decomposition techniques. By providing a framework generalizing interval based labeling schemes, we obtain a simple, yet asymptotically optimal solution to the problem. Furthermore, our labeling scheme is attained by a small modification of the original 2lgn2 \lg n solution.Comment: 12 pages, 1 figure. To appear at ICALP'1

    Distance labeling schemes for trees

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    We consider distance labeling schemes for trees: given a tree with nn nodes, label the nodes with binary strings such that, given the labels of any two nodes, one can determine, by looking only at the labels, the distance in the tree between the two nodes. A lower bound by Gavoille et. al. (J. Alg. 2004) and an upper bound by Peleg (J. Graph Theory 2000) establish that labels must use Θ(log2n)\Theta(\log^2 n) bits\footnote{Throughout this paper we use log\log for log2\log_2.}. Gavoille et. al. (ESA 2001) show that for very small approximate stretch, labels use Θ(lognloglogn)\Theta(\log n \log \log n) bits. Several other papers investigate various variants such as, for example, small distances in trees (Alstrup et. al., SODA'03). We improve the known upper and lower bounds of exact distance labeling by showing that 14log2n\frac{1}{4} \log^2 n bits are needed and that 12log2n\frac{1}{2} \log^2 n bits are sufficient. We also give (1+ϵ1+\epsilon)-stretch labeling schemes using Θ(logn)\Theta(\log n) bits for constant ϵ>0\epsilon>0. (1+ϵ1+\epsilon)-stretch labeling schemes with polylogarithmic label size have previously been established for doubling dimension graphs by Talwar (STOC 2004). In addition, we present matching upper and lower bounds for distance labeling for caterpillars, showing that labels must have size 2lognΘ(loglogn)2\log n - \Theta(\log\log n). For simple paths with kk nodes and edge weights in [1,n][1,n], we show that labels must have size k1klogn+Θ(logk)\frac{k-1}{k}\log n+\Theta(\log k)

    Simpler, faster and shorter labels for distances in graphs

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    We consider how to assign labels to any undirected graph with n nodes such that, given the labels of two nodes and no other information regarding the graph, it is possible to determine the distance between the two nodes. The challenge in such a distance labeling scheme is primarily to minimize the maximum label lenght and secondarily to minimize the time needed to answer distance queries (decoding). Previous schemes have offered different trade-offs between label lengths and query time. This paper presents a simple algorithm with shorter labels and shorter query time than any previous solution, thereby improving the state-of-the-art with respect to both label length and query time in one single algorithm. Our solution addresses several open problems concerning label length and decoding time and is the first improvement of label length for more than three decades. More specifically, we present a distance labeling scheme with label size (log 3)/2 + o(n) (logarithms are in base 2) and O(1) decoding time. This outperforms all existing results with respect to both size and decoding time, including Winkler's (Combinatorica 1983) decade-old result, which uses labels of size (log 3)n and O(n/log n) decoding time, and Gavoille et al. (SODA'01), which uses labels of size 11n + o(n) and O(loglog n) decoding time. In addition, our algorithm is simpler than the previous ones. In the case of integral edge weights of size at most W, we present almost matching upper and lower bounds for label sizes. For r-additive approximation schemes, where distances can be off by an additive constant r, we give both upper and lower bounds. In particular, we present an upper bound for 1-additive approximation schemes which, in the unweighted case, has the same size (ignoring second order terms) as an adjacency scheme: n/2. We also give results for bipartite graphs and for exact and 1-additive distance oracles

    Near-Optimal Induced Universal Graphs for Bounded Degree Graphs

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    A graph U is an induced universal graph for a family F of graphs if every graph in F is a vertex-induced subgraph of U. We give upper and lower bounds for the size of induced universal graphs for the family of graphs with n vertices of maximum degree D. Our new bounds improve several previous results except for the special cases where D is either near-constant or almost n/2. For constant even D Butler [Graphs and Combinatorics 2009] has shown O(n^(D/2)) and recently Alon and Nenadov [SODA 2017] showed the same bound for constant odd D. For constant D Butler also gave a matching lower bound. For generals graphs, which corresponds to D = n, Alon [Geometric and Functional Analysis, to appear] proved the existence of an induced universal graph with (1+o(1)) cdot 2^((n-1)/2) vertices, leading to a smaller constant than in the previously best known bound of 16 * 2^(n/2) by Alstrup, Kaplan, Thorup, and Zwick [STOC 2015]. In this paper we give the following lower and upper bound of binom(floor(n/2))(floor(D/2)) * n^(-O(1)) and binom(floor(n/2))(floor(D/2)) * 2^(O(sqrt(D log D) * log(n/D))), respectively, where the upper bound is the main contribution. The proof that it is an induced universal graph relies on a randomized argument. We also give a deterministic upper bound of O(n^k / (k-1)!). These upper bounds are the best known when D <= n/2 - tilde-Omega(n^(3/4)) and either D is even and D = omega(1) or D is odd and D = omega(log n/log log n). In this range we improve asymptotically on the previous best known results by Butler [Graphs and Combinatorics 2009], Esperet, Arnaud and Ochem [IPL 2008], Adjiashvili and Rotbart [ICALP 2014], Alon and Nenadov [SODA 2017], and Alon [Geometric and Functional Analysis, to appear]

    Adjacency labeling schemes and induced-universal graphs

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    We describe a way of assigning labels to the vertices of any undirected graph on up to nn vertices, each composed of n/2+O(1)n/2+O(1) bits, such that given the labels of two vertices, and no other information regarding the graph, it is possible to decide whether or not the vertices are adjacent in the graph. This is optimal, up to an additive constant, and constitutes the first improvement in almost 50 years of an n/2+O(logn)n/2+O(\log n) bound of Moon. As a consequence, we obtain an induced-universal graph for nn-vertex graphs containing only O(2n/2)O(2^{n/2}) vertices, which is optimal up to a multiplicative constant, solving an open problem of Vizing from 1968. We obtain similar tight results for directed graphs, tournaments and bipartite graphs

    Near-Optimal Induced Universal Graphs for Bounded Degree Graphs

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    A graph UU is an induced universal graph for a family FF of graphs if every graph in FF is a vertex-induced subgraph of UU. For the family of all undirected graphs on nn vertices Alstrup, Kaplan, Thorup, and Zwick [STOC 2015] give an induced universal graph with O ⁣(2n/2)O\!\left(2^{n/2}\right) vertices, matching a lower bound by Moon [Proc. Glasgow Math. Assoc. 1965]. Let k=D/2k= \lceil D/2 \rceil. Improving asymptotically on previous results by Butler [Graphs and Combinatorics 2009] and Esperet, Arnaud and Ochem [IPL 2008], we give an induced universal graph with O ⁣(k2kk!nk)O\!\left(\frac{k2^k}{k!}n^k \right) vertices for the family of graphs with nn vertices of maximum degree DD. For constant DD, Butler gives a lower bound of Ω ⁣(nD/2)\Omega\!\left(n^{D/2}\right). For an odd constant D3D\geq 3, Esperet et al. and Alon and Capalbo [SODA 2008] give a graph with O ⁣(nk1D)O\!\left(n^{k-\frac{1}{D}}\right) vertices. Using their techniques for any (including constant) even values of DD gives asymptotically worse bounds than we present. For large DD, i.e. when D=Ω(log3n)D = \Omega\left(\log^3 n\right), the previous best upper bound was (nD/2)nO(1){n\choose\lceil D/2\rceil} n^{O(1)} due to Adjiashvili and Rotbart [ICALP 2014]. We give upper and lower bounds showing that the size is (n/2D/2)2±O~(D){\lfloor n/2\rfloor\choose\lfloor D/2 \rfloor}2^{\pm\tilde{O}\left(\sqrt{D}\right)}. Hence the optimal size is 2O~(D)2^{\tilde{O}(D)} and our construction is within a factor of 2O~(D)2^{\tilde{O}\left(\sqrt{D}\right)} from this. The previous results were larger by at least a factor of 2Ω(D)2^{\Omega(D)}. As a part of the above, proving a conjecture by Esperet et al., we construct an induced universal graph with 2n12n-1 vertices for the family of graphs with max degree 22. In addition, we give results for acyclic graphs with max degree 22 and cycle graphs. Our results imply the first labeling schemes that for any DD are at most o(n)o(n) bits from optimal

    Data Structures for Node Connectivity Queries

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