262 research outputs found
A Linear-Size Logarithmic Stretch Path-Reporting Distance Oracle for General Graphs
In 2001 Thorup and Zwick devised a distance oracle, which given an -vertex
undirected graph and a parameter , has size . Upon a query
their oracle constructs a -approximate path between
and . The query time of the Thorup-Zwick's oracle is , and it was
subsequently improved to by Chechik. A major drawback of the oracle of
Thorup and Zwick is that its space is . Mendel and Naor
devised an oracle with space and stretch , but their
oracle can only report distance estimates and not actual paths. In this paper
we devise a path-reporting distance oracle with size , stretch
and query time , for an arbitrarily small .
In particular, our oracle can provide logarithmic stretch using linear size.
Another variant of our oracle has size , polylogarithmic
stretch, and query time .
For unweighted graphs we devise a distance oracle with multiplicative stretch
, additive stretch , for a function , space
, and query time , for an arbitrarily
small constant . The tradeoff between multiplicative stretch and
size in these oracles is far below girth conjecture threshold (which is stretch
and size ). Breaking the girth conjecture tradeoff is
achieved by exhibiting a tradeoff of different nature between additive stretch
and size . A similar type of tradeoff was exhibited by
a construction of -spanners due to Elkin and Peleg.
However, so far -spanners had no counterpart in the
distance oracles' world.
An important novel tool that we develop on the way to these results is a
{distance-preserving path-reporting oracle}
Pruning based Distance Sketches with Provable Guarantees on Random Graphs
Measuring the distances between vertices on graphs is one of the most
fundamental components in network analysis. Since finding shortest paths
requires traversing the graph, it is challenging to obtain distance information
on large graphs very quickly. In this work, we present a preprocessing
algorithm that is able to create landmark based distance sketches efficiently,
with strong theoretical guarantees. When evaluated on a diverse set of social
and information networks, our algorithm significantly improves over existing
approaches by reducing the number of landmarks stored, preprocessing time, or
stretch of the estimated distances.
On Erd\"{o}s-R\'{e}nyi graphs and random power law graphs with degree
distribution exponent , our algorithm outputs an exact distance
data structure with space between and
depending on the value of , where is the number of vertices. We
complement the algorithm with tight lower bounds for Erdos-Renyi graphs and the
case when is close to two.Comment: Full version for the conference paper to appear in The Web
Conference'1
Routing in Polygonal Domains
We consider the problem of routing a data packet through the visibility graph of a polygonal domain P with n vertices and h holes. We may preprocess P to obtain a label and a routing table for each vertex. Then, we must be able to route a data packet between any two vertices p and q of Pwhere each step must use only the label of the target node q and the routing table of the current node.
For any fixed eps > 0, we pre ent a routing scheme that always achieves a routing path that exceeds the shortest path by a factor of at most 1 + eps. The labels have O(log n) bits, and the routing tables are of size O((eps^{-1} + h) log n). The preprocessing time is O(n^2 log n + hn^2 + eps^{-1}hn). It can be improved to O(n 2 + eps^{-1}n) for simple polygons
Hardness of Exact Distance Queries in Sparse Graphs Through Hub Labeling
A distance labeling scheme is an assignment of bit-labels to the vertices of
an undirected, unweighted graph such that the distance between any pair of
vertices can be decoded solely from their labels. An important class of
distance labeling schemes is that of hub labelings, where a node
stores its distance to the so-called hubs , chosen so that for
any there is belonging to some shortest
path. Notice that for most existing graph classes, the best distance labelling
constructions existing use at some point a hub labeling scheme at least as a
key building block. Our interest lies in hub labelings of sparse graphs, i.e.,
those with , for which we show a lowerbound of
for the average size of the hubsets.
Additionally, we show a hub-labeling construction for sparse graphs of average
size for some , where is the
so-called Ruzsa-Szemer{\'e}di function, linked to structure of induced
matchings in dense graphs. This implies that further improving the lower bound
on hub labeling size to would require a
breakthrough in the study of lower bounds on , which have resisted
substantial improvement in the last 70 years. For general distance labeling of
sparse graphs, we show a lowerbound of , where is the communication complexity of the
Sum-Index problem over . Our results suggest that the best achievable
hub-label size and distance-label size in sparse graphs may be
for some
Routing in Histograms
Let be an -monotone orthogonal polygon with vertices. We call
a simple histogram if its upper boundary is a single edge; and a double
histogram if it has a horizontal chord from the left boundary to the right
boundary. Two points and in are co-visible if and only if the
(axis-parallel) rectangle spanned by and completely lies in . In the
-visibility graph of , we connect two vertices of with an edge
if and only if they are co-visible.
We consider routing with preprocessing in . We may preprocess to
obtain a label and a routing table for each vertex of . Then, we must be
able to route a packet between any two vertices and of , where each
step may use only the label of the target node , the routing table and
neighborhood of the current node, and the packet header.
We present a routing scheme for double histograms that sends any data packet
along a path whose length is at most twice the (unweighted) shortest path
distance between the endpoints. In our scheme, the labels, routing tables, and
headers need bits. For the case of simple histograms, we obtain a
routing scheme with optimal routing paths, -bit labels, one-bit
routing tables, and no headers.Comment: 18 pages, 11 figure
All-Pairs Approximate Shortest Paths and Distance Oracle Preprocessing
Given an undirected, unweighted graph G on n nodes, there is an O(n^2*poly log(n))-time algorithm that computes a data structure called distance oracle of size O(n^{5/3}*poly log(n)) answering approximate distance queries in constant time. For nodes at distance d the distance estimate is between d and 2d + 1.
This new distance oracle improves upon the oracles of Patrascu and Roditty (FOCS 2010), Abraham and Gavoille (DISC 2011), and Agarwal and Brighten Godfrey (PODC 2013) in terms of preprocessing time, and upon the oracle of Baswana and Sen (SODA 2004) in terms of stretch. The running time analysis is tight (up to logarithmic factors) due to a recent lower bound of Abboud and Bodwin (STOC 2016).
Techniques include dominating sets, sampling, balls, and spanners, and the main contribution lies in the way these techniques are combined. Perhaps the most interesting aspect from a technical point of view is the application of a spanner without incurring its constant additive stretch penalty
Improved Distance Oracles and Spanners for Vertex-Labeled Graphs
Consider an undirected weighted graph G=(V,E) with |V|=n and |E|=m, where
each vertex v is assigned a label from a set L of \ell labels. We show how to
construct a compact distance oracle that can answer queries of the form: "what
is the distance from v to the closest lambda-labeled node" for a given node v
in V and label lambda in L.
This problem was introduced by Hermelin, Levy, Weimann and Yuster [ICALP
2011] where they present several results for this problem. In the first result,
they show how to construct a vertex-label distance oracle of expected size
O(kn^{1+1/k}) with stretch (4k - 5) and query time O(k). In a second result,
they show how to reduce the size of the data structure to O(kn \ell^{1/k}) at
the expense of a huge stretch, the stretch of this construction grows
exponentially in k, (2^k-1). In the third result they present a dynamic
vertex-label distance oracle that is capable of handling label changes in a
sub-linear time. The stretch of this construction is also exponential in k, (2
3^{k-1}+1).
We manage to significantly improve the stretch of their constructions,
reducing the dependence on k from exponential to polynomial (4k-5), without
requiring any tradeoff regarding any of the other variables.
In addition, we introduce the notion of vertex-label spanners: subgraphs that
preserve distances between every node v and label lambda. We present an
efficient construction for vertex-label spanners with stretch-size tradeoff
close to optimal
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