1,322 research outputs found
On relaxing the constraints in pairwise compatibility graphs
A graph is called a pairwise compatibility graph (PCG) if there exists an
edge weighted tree and two non-negative real numbers and
such that each leaf of corresponds to a vertex
and there is an edge if and only if where is the sum of the weights of the edges on
the unique path from to in . In this paper we analyze the class
of PCG in relation with two particular subclasses resulting from the the cases
where \dmin=0 (LPG) and \dmax=+\infty (mLPG). In particular, we show that
the union of LPG and mLPG does not coincide with the whole class PCG, their
intersection is not empty, and that neither of the classes LPG and mLPG is
contained in the other. Finally, as the graphs we deal with belong to the more
general class of split matrogenic graphs, we focus on this class of graphs for
which we try to establish the membership to the PCG class.Comment: 12 pages, 7 figure
Elastic Registration of Geodesic Vascular Graphs
Vascular graphs can embed a number of high-level features, from morphological
parameters, to functional biomarkers, and represent an invaluable tool for
longitudinal and cross-sectional clinical inference. This, however, is only
feasible when graphs are co-registered together, allowing coherent multiple
comparisons. The robust registration of vascular topologies stands therefore as
key enabling technology for group-wise analyses. In this work, we present an
end-to-end vascular graph registration approach, that aligns networks with
non-linear geometries and topological deformations, by introducing a novel
overconnected geodesic vascular graph formulation, and without enforcing any
anatomical prior constraint. The 3D elastic graph registration is then
performed with state-of-the-art graph matching methods used in computer vision.
Promising results of vascular matching are found using graphs from synthetic
and real angiographies. Observations and future designs are discussed towards
potential clinical applications
All graphs with at most seven vertices are Pairwise Compatibility Graphs
A graph is called a pairwise compatibility graph (PCG) if there exists an
edge-weighted tree and two non-negative real numbers and
such that each leaf of corresponds to a vertex
and there is an edge if and only if where is the sum of the weights of the
edges on the unique path from to in .
In this note, we show that all the graphs with at most seven vertices are
PCGs. In particular all these graphs except for the wheel on 7 vertices
are PCGs of a particular structure of a tree: a centipede.Comment: 8 pages, 2 figure
Relating threshold tolerance graphs to other graph classes
A graph G=(V, E) is a threshold tolerance if it is possible to associate weights and tolerances with each node of G so that two nodes are adjacent exactly when the sum of their weights exceeds either one of their tolerances. Threshold tolerance graphs are a special case of the well-known class of tolerance graphs and generalize the class of threshold graphs which are also extensively studied in literature. In this note we relate the threshold tolerance graphs with other important graph classes. In particular we show that threshold tolerance graphs are a proper subclass of co-strongly chordal graphs and strictly include the class of co-interval graphs. To this purpose, we exploit the relation with another graph class, min leaf power graphs (mLPGs)
The matching relaxation for a class of generalized set partitioning problems
This paper introduces a discrete relaxation for the class of combinatorial
optimization problems which can be described by a set partitioning formulation
under packing constraints. We present two combinatorial relaxations based on
computing maximum weighted matchings in suitable graphs. Besides providing dual
bounds, the relaxations are also used on a variable reduction technique and a
matheuristic. We show how that general method can be tailored to sample
applications, and also perform a successful computational evaluation with
benchmark instances of a problem in maritime logistics.Comment: 33 pages. A preliminary (4-page) version of this paper was presented
at CTW 2016 (Cologne-Twente Workshop on Graphs and Combinatorial
Optimization), with proceedings on Electronic Notes in Discrete Mathematic
Clustering by soft-constraint affinity propagation: Applications to gene-expression data
Motivation: Similarity-measure based clustering is a crucial problem
appearing throughout scientific data analysis. Recently, a powerful new
algorithm called Affinity Propagation (AP) based on message-passing techniques
was proposed by Frey and Dueck \cite{Frey07}. In AP, each cluster is identified
by a common exemplar all other data points of the same cluster refer to, and
exemplars have to refer to themselves. Albeit its proved power, AP in its
present form suffers from a number of drawbacks. The hard constraint of having
exactly one exemplar per cluster restricts AP to classes of regularly shaped
clusters, and leads to suboptimal performance, {\it e.g.}, in analyzing gene
expression data. Results: This limitation can be overcome by relaxing the AP
hard constraints. A new parameter controls the importance of the constraints
compared to the aim of maximizing the overall similarity, and allows to
interpolate between the simple case where each data point selects its closest
neighbor as an exemplar and the original AP. The resulting soft-constraint
affinity propagation (SCAP) becomes more informative, accurate and leads to
more stable clustering. Even though a new {\it a priori} free-parameter is
introduced, the overall dependence of the algorithm on external tuning is
reduced, as robustness is increased and an optimal strategy for parameter
selection emerges more naturally. SCAP is tested on biological benchmark data,
including in particular microarray data related to various cancer types. We
show that the algorithm efficiently unveils the hierarchical cluster structure
present in the data sets. Further on, it allows to extract sparse gene
expression signatures for each cluster.Comment: 11 pages, supplementary material:
http://isiosf.isi.it/~weigt/scap_supplement.pd
- …