39,216 research outputs found
Combinatorial and stochastic properties of ranked tree-child networks
Tree-child networks are a recently-described class of directed acyclic graphs
that have risen to prominence in phylogenetics (the study of evolutionary trees
and networks). Although these networks have a number of attractive mathematical
properties, many combinatorial questions concerning them remain intractable. In
this paper, we show that endowing these networks with a biologically relevant
ranking structure yields mathematically tractable objects, which we term ranked
tree-child networks (RTCNs). We explain how to derive exact and explicit
combinatorial results concerning the enumeration and generation of these
networks. We also explore probabilistic questions concerning the properties of
RTCNs when they are sampled uniformly at random. These questions include the
lengths of random walks between the root and leaves (both from the root to the
leaves and from a leaf to the root); the distribution of the number of cherries
in the network; and sampling RTCNs conditional on displaying a given tree. We
also formulate a conjecture regarding the scaling limit of the process that
counts the number of lineages in the ancestry of a leaf. The main idea in this
paper, namely using ranking as a way to achieve combinatorial tractability, may
also extend to other classes of networks
Biased random walks on random graphs
These notes cover one of the topics programmed for the St Petersburg School
in Probability and Statistical Physics of June 2012.
The aim is to review recent mathematical developments in the field of random
walks in random environment. Our main focus will be on directionally transient
and reversible random walks on different types of underlying graph structures,
such as , trees and for .Comment: Survey based one of the topics programmed for the St Petersburg
School in Probability and Statistical Physics of June 2012. 64 pages, 16
figure
Evolutionary dynamics on any population structure
Evolution occurs in populations of reproducing individuals. The structure of
a biological population affects which traits evolve. Understanding evolutionary
game dynamics in structured populations is difficult. Precise results have been
absent for a long time, but have recently emerged for special structures where
all individuals have the same number of neighbors. But the problem of
determining which trait is favored by selection in the natural case where the
number of neighbors can vary, has remained open. For arbitrary selection
intensity, the problem is in a computational complexity class which suggests
there is no efficient algorithm. Whether there exists a simple solution for
weak selection was unanswered. Here we provide, surprisingly, a general formula
for weak selection that applies to any graph or social network. Our method uses
coalescent theory and relies on calculating the meeting times of random walks.
We can now evaluate large numbers of diverse and heterogeneous population
structures for their propensity to favor cooperation. We can also study how
small changes in population structure---graph surgery---affect evolutionary
outcomes. We find that cooperation flourishes most in societies that are based
on strong pairwise ties.Comment: 68 pages, 10 figure
Complex network classification using partially self-avoiding deterministic walks
Complex networks have attracted increasing interest from various fields of
science. It has been demonstrated that each complex network model presents
specific topological structures which characterize its connectivity and
dynamics. Complex network classification rely on the use of representative
measurements that model topological structures. Although there are a large
number of measurements, most of them are correlated. To overcome this
limitation, this paper presents a new measurement for complex network
classification based on partially self-avoiding walks. We validate the
measurement on a data set composed by 40.000 complex networks of four
well-known models. Our results indicate that the proposed measurement improves
correct classification of networks compared to the traditional ones
Discrete analogue computing with rotor-routers
Rotor-routing is a procedure for routing tokens through a network that can
implement certain kinds of computation. These computations are inherently
asynchronous (the order in which tokens are routed makes no difference) and
distributed (information is spread throughout the system). It is also possible
to efficiently check that a computation has been carried out correctly in less
time than the computation itself required, provided one has a certificate that
can itself be computed by the rotor-router network. Rotor-router networks can
be viewed as both discrete analogues of continuous linear systems and
deterministic analogues of stochastic processes.Comment: To appear in Chaos Special Focus Issue on Intrinsic and Designed
Computatio
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