39,811 research outputs found
Strong Amplifiers of Natural Selection: Proofs
We consider the modified Moran process on graphs to study the spread of
genetic and cultural mutations on structured populations. An initial mutant
arises either spontaneously (aka \emph{uniform initialization}), or during
reproduction (aka \emph{temperature initialization}) in a population of
individuals, and has a fixed fitness advantage over the residents of the
population. The fixation probability is the probability that the mutant takes
over the entire population. Graphs that ensure fixation probability of~1 in the
limit of infinite populations are called \emph{strong amplifiers}. Previously,
only a few examples of strong amplifiers were known for uniform initialization,
whereas no strong amplifiers were known for temperature initialization.
In this work, we study necessary and sufficient conditions for strong
amplification, and prove negative and positive results. We show that for
temperature initialization, graphs that are unweighted and/or self-loop-free
have fixation probability upper-bounded by , where is a
function linear in . Similarly, we show that for uniform initialization,
bounded-degree graphs that are unweighted and/or self-loop-free have fixation
probability upper-bounded by , where is the degree bound and
a function linear in . Our main positive result complements these
negative results, and is as follows: every family of undirected graphs with
(i)~self loops and (ii)~diameter bounded by , for some fixed
, can be assigned weights that makes it a strong amplifier, both
for uniform and temperature initialization
A survey of statistical network models
Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions, and pay special attention to the interpretation of
parameters and their estimation. We end with a description of some open
problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference
Random Topologies and the emergence of cooperation: the role of short-cuts
We study in detail the role of short-cuts in promoting the emergence of
cooperation in a network of agents playing the Prisoner's Dilemma Game (PDG).
We introduce a model whose topology interpolates between the one-dimensional
euclidean lattice (a ring) and the complete graph by changing the value of one
parameter (the probability p to add a link between two nodes not already
connected in the euclidean configuration). We show that there is a region of
values of p in which cooperation is largely enhanced, whilst for smaller values
of p only a few cooperators are present in the final state, and for p
\rightarrow 1- cooperation is totally suppressed. We present analytical
arguments that provide a very plausible interpretation of the simulation
results, thus unveiling the mechanism by which short-cuts contribute to promote
(or suppress) cooperation
Batch kernel SOM and related Laplacian methods for social network analysis
Large graphs are natural mathematical models for describing the structure of
the data in a wide variety of fields, such as web mining, social networks,
information retrieval, biological networks, etc. For all these applications,
automatic tools are required to get a synthetic view of the graph and to reach
a good understanding of the underlying problem. In particular, discovering
groups of tightly connected vertices and understanding the relations between
those groups is very important in practice. This paper shows how a kernel
version of the batch Self Organizing Map can be used to achieve these goals via
kernels derived from the Laplacian matrix of the graph, especially when it is
used in conjunction with more classical methods based on the spectral analysis
of the graph. The proposed method is used to explore the structure of a
medieval social network modeled through a weighted graph that has been directly
built from a large corpus of agrarian contracts
A statistical network analysis of the HIV/AIDS epidemics in Cuba
The Cuban contact-tracing detection system set up in 1986 allowed the
reconstruction and analysis of the sexual network underlying the epidemic
(5,389 vertices and 4,073 edges, giant component of 2,386 nodes and 3,168
edges), shedding light onto the spread of HIV and the role of contact-tracing.
Clustering based on modularity optimization provides a better visualization and
understanding of the network, in combination with the study of covariates. The
graph has a globally low but heterogeneous density, with clusters of high
intraconnectivity but low interconnectivity. Though descriptive, our results
pave the way for incorporating structure when studying stochastic SIR epidemics
spreading on social networks
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