66,851 research outputs found
Data Reduction for Graph Coloring Problems
This paper studies the kernelization complexity of graph coloring problems
with respect to certain structural parameterizations of the input instances. We
are interested in how well polynomial-time data reduction can provably shrink
instances of coloring problems, in terms of the chosen parameter. It is well
known that deciding 3-colorability is already NP-complete, hence parameterizing
by the requested number of colors is not fruitful. Instead, we pick up on a
research thread initiated by Cai (DAM, 2003) who studied coloring problems
parameterized by the modification distance of the input graph to a graph class
on which coloring is polynomial-time solvable; for example parameterizing by
the number k of vertex-deletions needed to make the graph chordal. We obtain
various upper and lower bounds for kernels of such parameterizations of
q-Coloring, complementing Cai's study of the time complexity with respect to
these parameters.
Our results show that the existence of polynomial kernels for q-Coloring
parameterized by the vertex-deletion distance to a graph class F is strongly
related to the existence of a function f(q) which bounds the number of vertices
which are needed to preserve the NO-answer to an instance of q-List-Coloring on
F.Comment: Author-accepted manuscript of the article that will appear in the FCT
2011 special issue of Information & Computatio
Generalised compositionality in graph transformation
We present a notion of composition applying both to graphs and to rules, based on graph and rule interfaces along which they are glued. The current paper generalises a previous result in two different ways. Firstly, rules do not have to form pullbacks with their interfaces; this enables graph passing between components, meaning that components may ālearnā and āforgetā subgraphs through communication with other components. Secondly, composition is no longer binary; instead, it can be repeated for an arbitrary number of components
Avoiding Unnecessary Information Loss: Correct and Efficient Model Synchronization Based on Triple Graph Grammars
Model synchronization, i.e., the task of restoring consistency between two
interrelated models after a model change, is a challenging task. Triple Graph
Grammars (TGGs) specify model consistency by means of rules that describe how
to create consistent pairs of models. These rules can be used to automatically
derive further rules, which describe how to propagate changes from one model to
the other or how to change one model in such a way that propagation is
guaranteed to be possible. Restricting model synchronization to these derived
rules, however, may lead to unnecessary deletion and recreation of model
elements during change propagation. This is inefficient and may cause
unnecessary information loss, i.e., when deleted elements contain information
that is not represented in the second model, this information cannot be
recovered easily. Short-cut rules have recently been developed to avoid
unnecessary information loss by reusing existing model elements. In this paper,
we show how to automatically derive (short-cut) repair rules from short-cut
rules to propagate changes such that information loss is avoided and model
synchronization is accelerated. The key ingredients of our rule-based model
synchronization process are these repair rules and an incremental pattern
matcher informing about suitable applications of them. We prove the termination
and the correctness of this synchronization process and discuss its
completeness. As a proof of concept, we have implemented this synchronization
process in eMoflon, a state-of-the-art model transformation tool with inherent
support of bidirectionality. Our evaluation shows that repair processes based
on (short-cut) repair rules have considerably decreased information loss and
improved performance compared to former model synchronization processes based
on TGGs.Comment: 33 pages, 20 figures, 3 table
Face pairing graphs and 3-manifold enumeration
The face pairing graph of a 3-manifold triangulation is a 4-valent graph
denoting which tetrahedron faces are identified with which others. We present a
series of properties that must be satisfied by the face pairing graph of a
closed minimal P^2-irreducible triangulation. In addition we present
constraints upon the combinatorial structure of such a triangulation that can
be deduced from its face pairing graph. These results are then applied to the
enumeration of closed minimal P^2-irreducible 3-manifold triangulations,
leading to a significant improvement in the performance of the enumeration
algorithm. Results are offered for both orientable and non-orientable
triangulations.Comment: 30 pages, 57 figures; v2: clarified some passages and generalised the
final theorem to the non-orientable case; v3: fixed a flaw in the proof of
the conical face lemm
Reactive Systems over Cospans
The theory of reactive systems, introduced by Leifer and Milner and previously extended by the authors, allows the derivation of well-behaved labelled transition systems (LTS) for semantic models with an underlying reduction semantics. The derivation procedure requires the presence of certain colimits (or, more usually and generally, bicolimits) which need to be constructed separately within each model. In this paper, we offer a general construction of such bicolimits in a class of bicategories of cospans. The construction sheds light on as well as extends Ehrig and Konigās rewriting via borrowed contexts and opens the way to a unified treatment of several applications
Craquelure as a Graph: Application of Image Processing and Graph Neural Networks to the Description of Fracture Patterns
Cracks on a painting is not a defect but an inimitable signature of an
artwork which can be used for origin examination, aging monitoring, damage
identification, and even forgery detection. This work presents the development
of a new methodology and corresponding toolbox for the extraction and
characterization of information from an image of a craquelure pattern.
The proposed approach processes craquelure network as a graph. The graph
representation captures the network structure via mutual organization of
junctions and fractures. Furthermore, it is invariant to any geometrical
distortions. At the same time, our tool extracts the properties of each node
and edge individually, which allows to characterize the pattern statistically.
We illustrate benefits from the graph representation and statistical features
individually using novel Graph Neural Network and hand-crafted descriptors
correspondingly. However, we also show that the best performance is achieved
when both techniques are merged into one framework. We perform experiments on
the dataset for paintings' origin classification and demonstrate that our
approach outperforms existing techniques by a large margin.Comment: Published in ICCV 2019 Workshop
Efficient Identification of Equivalences in Dynamic Graphs and Pedigree Structures
We propose a new framework for designing test and query functions for complex
structures that vary across a given parameter such as genetic marker position.
The operations we are interested in include equality testing, set operations,
isolating unique states, duplication counting, or finding equivalence classes
under identifiability constraints. A motivating application is locating
equivalence classes in identity-by-descent (IBD) graphs, graph structures in
pedigree analysis that change over genetic marker location. The nodes of these
graphs are unlabeled and identified only by their connecting edges, a
constraint easily handled by our approach. The general framework introduced is
powerful enough to build a range of testing functions for IBD graphs, dynamic
populations, and other structures using a minimal set of operations. The
theoretical and algorithmic properties of our approach are analyzed and proved.
Computational results on several simulations demonstrate the effectiveness of
our approach.Comment: Code for paper available at
http://www.stat.washington.edu/~hoytak/code/hashreduc
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