24,511 research outputs found
Graphical Markov models: overview
We describe how graphical Markov models started to emerge in the last 40
years, based on three essential concepts that had been developed independently
more than a century ago. Sequences of joint or single regressions and their
regression graphs are singled out as being best suited for analyzing
longitudinal data and for tracing developmental pathways. Interpretations are
illustrated using two sets of data and some of the more recent, important
results for sequences of regressions are summarized.Comment: 22 pages, 9 figure
Edge-Stable Equimatchable Graphs
A graph is \emph{equimatchable} if every maximal matching of has the
same cardinality. We are interested in equimatchable graphs such that the
removal of any edge from the graph preserves the equimatchability. We call an
equimatchable graph \emph{edge-stable} if , that is the
graph obtained by the removal of edge from , is also equimatchable for
any . After noticing that edge-stable equimatchable graphs are
either 2-connected factor-critical or bipartite, we characterize edge-stable
equimatchable graphs. This characterization yields an time recognition algorithm. Lastly, we introduce and shortly
discuss the related notions of edge-critical, vertex-stable and vertex-critical
equimatchable graphs. In particular, we emphasize the links between our work
and the well-studied notion of shedding vertices, and point out some open
questions
Robustness: a New Form of Heredity Motivated by Dynamic Networks
We investigate a special case of hereditary property in graphs, referred to
as {\em robustness}. A property (or structure) is called robust in a graph
if it is inherited by all the connected spanning subgraphs of . We motivate
this definition using two different settings of dynamic networks. The first
corresponds to networks of low dynamicity, where some links may be permanently
removed so long as the network remains connected. The second corresponds to
highly-dynamic networks, where communication links appear and disappear
arbitrarily often, subject only to the requirement that the entities are
temporally connected in a recurrent fashion ({\it i.e.} they can always reach
each other through temporal paths). Each context induces a different
interpretation of the notion of robustness.
We start by motivating the definition and discussing the two interpretations,
after what we consider the notion independently from its interpretation, taking
as our focus the robustness of {\em maximal independent sets} (MIS). A graph
may or may not admit a robust MIS. We characterize the set of graphs \forallMIS
in which {\em all} MISs are robust. Then, we turn our attention to the graphs
that {\em admit} a robust MIS (\existsMIS). This class has a more complex
structure; we give a partial characterization in terms of elementary graph
properties, then a complete characterization by means of a (polynomial time)
decision algorithm that accepts if and only if a robust MIS exists. This
algorithm can be adapted to construct such a solution if one exists
Message passing for vertex covers
Constructing a minimal vertex cover of a graph can be seen as a prototype for
a combinatorial optimization problem under hard constraints. In this paper, we
develop and analyze message passing techniques, namely warning and survey
propagation, which serve as efficient heuristic algorithms for solving these
computational hard problems. We show also, how previously obtained results on
the typical-case behavior of vertex covers of random graphs can be recovered
starting from the message passing equations, and how they can be extended.Comment: 25 pages, 9 figures - version accepted for publication in PR
Joint estimation of multiple related biological networks
Graphical models are widely used to make inferences concerning interplay in
multivariate systems. In many applications, data are collected from multiple
related but nonidentical units whose underlying networks may differ but are
likely to share features. Here we present a hierarchical Bayesian formulation
for joint estimation of multiple networks in this nonidentically distributed
setting. The approach is general: given a suitable class of graphical models,
it uses an exchangeability assumption on networks to provide a corresponding
joint formulation. Motivated by emerging experimental designs in molecular
biology, we focus on time-course data with interventions, using dynamic
Bayesian networks as the graphical models. We introduce a computationally
efficient, deterministic algorithm for exact joint inference in this setting.
We provide an upper bound on the gains that joint estimation offers relative to
separate estimation for each network and empirical results that support and
extend the theory, including an extensive simulation study and an application
to proteomic data from human cancer cell lines. Finally, we describe
approximations that are still more computationally efficient than the exact
algorithm and that also demonstrate good empirical performance.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS761 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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