6,943 research outputs found
Near-optimal adjacency labeling scheme for power-law graphs
An adjacency labeling scheme is a method that assigns labels to the vertices
of a graph such that adjacency between vertices can be inferred directly from
the assigned label, without using a centralized data structure. We devise
adjacency labeling schemes for the family of power-law graphs. This family that
has been used to model many types of networks, e.g. the Internet AS-level
graph. Furthermore, we prove an almost matching lower bound for this family. We
also provide an asymptotically near- optimal labeling scheme for sparse graphs.
Finally, we validate the efficiency of our labeling scheme by an experimental
evaluation using both synthetic data and real-world networks of up to hundreds
of thousands of vertices
A simple and optimal ancestry labeling scheme for trees
We present a ancestry labeling scheme for trees. The
problem was first presented by Kannan et al. [STOC 88'] along with a simple 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 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 solution.Comment: 12 pages, 1 figure. To appear at ICALP'1
Sublinear Distance Labeling
A distance labeling scheme labels the nodes of a graph with binary
strings such that, given the labels of any two nodes, one can determine the
distance in the graph between the two nodes by looking only at the labels. A
-preserving distance labeling scheme only returns precise distances between
pairs of nodes that are at distance at least from each other. In this paper
we consider distance labeling schemes for the classical case of unweighted
graphs with both directed and undirected edges.
We present a bit -preserving distance labeling
scheme, improving the previous bound by Bollob\'as et. al. [SIAM J. Discrete
Math. 2005]. We also give an almost matching lower bound of
. With our -preserving distance labeling scheme as a
building block, we additionally achieve the following results:
1. We present the first distance labeling scheme of size for sparse
graphs (and hence bounded degree graphs). This addresses an open problem by
Gavoille et. al. [J. Algo. 2004], hereby separating the complexity from
distance labeling in general graphs which require bits, Moon [Proc.
of Glasgow Math. Association 1965].
2. For approximate -additive labeling schemes, that return distances
within an additive error of we show a scheme of size for .
This improves on the current best bound of by
Alstrup et. al. [SODA 2016] for sub-polynomial , and is a generalization of
a result by Gawrychowski et al. [arXiv preprint 2015] who showed this for
.Comment: A preliminary version of this paper appeared at ESA'1
Labeling Schemes for Bounded Degree Graphs
We investigate adjacency labeling schemes for graphs of bounded degree
. In particular, we present an optimal (up to an additive
constant) adjacency labeling scheme for bounded degree trees.
The latter scheme is derived from a labeling scheme for bounded degree
outerplanar graphs. Our results complement a similar bound recently obtained
for bounded depth trees [Fraigniaud and Korman, SODA 10], and may provide new
insights for closing the long standing gap for adjacency in trees [Alstrup and
Rauhe, FOCS 02]. We also provide improved labeling schemes for bounded degree
planar graphs. Finally, we use combinatorial number systems and present an
improved adjacency labeling schemes for graphs of bounded degree with
SCOOTER: A compact and scalable dynamic labeling scheme for XML updates
Although dynamic labeling schemes for XML have been the
focus of recent research activity, there are significant challenges still to be overcome. In particular, though there are labeling schemes that ensure a compact label representation when creating an XML document, when the document is subject to repeated and arbitrary deletions and insertions, the labels grow rapidly and consequently have a significant impact on query and update performance. We review the outstanding issues todate and in this paper we propose SCOOTER - a new dynamic labeling scheme for XML. The new labeling scheme can completely avoid relabeling
existing labels. In particular, SCOOTER can handle frequently skewed insertions gracefully. Theoretical analysis and experimental results confirm the scalability, compact representation, efficient growth rate and performance of SCOOTER in comparison to existing dynamic labeling schemes
Does Mandatory Labeling of Genetically Modified Food Grant Consumers the Right to Know? Evidence from an Economic Experiment
Opponents of the voluntary labeling scheme for genetically modified (GM) food products often argue that consumers have the ?right to know? and therefore advocate mandatory labeling. In this paper we argue against this line of reasoning. Using experimental auctions conducted with a sample of the resident population of Mannheim, Germany, we show that the quality of the informational signal generated by a mandatory labeling scheme is affected by the number of labels in the market. If there are two labels, one for GM products and one for non-GM products, mandatory and voluntary labeling schemes generate a similar degree of uncertainty about the quality of products that do not carry a label. --labeling,genetically modified foods,consumer preferences,experimental auctions
On the Implicit Graph Conjecture
The implicit graph conjecture states that every sufficiently small,
hereditary graph class has a labeling scheme with a polynomial-time computable
label decoder. We approach this conjecture by investigating classes of label
decoders defined in terms of complexity classes such as P and EXP. For
instance, GP denotes the class of graph classes that have a labeling scheme
with a polynomial-time computable label decoder. Until now it was not even
known whether GP is a strict subset of GR. We show that this is indeed the case
and reveal a strict hierarchy akin to classical complexity. We also show that
classes such as GP can be characterized in terms of graph parameters. This
could mean that certain algorithmic problems are feasible on every graph class
in GP. Lastly, we define a more restrictive class of label decoders using
first-order logic that already contains many natural graph classes such as
forests and interval graphs. We give an alternative characterization of this
class in terms of directed acyclic graphs. By showing that some small,
hereditary graph class cannot be expressed with such label decoders a weaker
form of the implicit graph conjecture could be disproven.Comment: 13 pages, MFCS 201
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