3,518 research outputs found
Bicomponents and the robustness of networks to failure
A common definition of a robust connection between two nodes in a network
such as a communication network is that there should be at least two
independent paths connecting them, so that the failure of no single node in the
network causes them to become disconnected. This definition leads us naturally
to consider bicomponents, subnetworks in which every node has a robust
connection of this kind to every other. Here we study bicomponents in both real
and model networks using a combination of exact analytic techniques and
numerical methods. We show that standard network models predict there to be
essentially no small bicomponents in most networks, but there may be a giant
bicomponent, whose presence coincides with the presence of the ordinary giant
component, and we find that real networks seem by and large to follow this
pattern, although there are some interesting exceptions. We study the size of
the giant bicomponent as nodes in the network fail, using a specially developed
computer algorithm based on data trees, and find in some cases that our
networks are quite robust to failure, with large bicomponents persisting until
almost all vertices have been removed.Comment: 5 pages, 1 figure, 1 tabl
Random graphs with clustering
We offer a solution to a long-standing problem in the physics of networks,
the creation of a plausible, solvable model of a network that displays
clustering or transitivity -- the propensity for two neighbors of a network
node also to be neighbors of one another. We show how standard random graph
models can be generalized to incorporate clustering and give exact solutions
for various properties of the resulting networks, including sizes of network
components, size of the giant component if there is one, position of the phase
transition at which the giant component forms, and position of the phase
transition for percolation on the network.Comment: 5 pages, 2 figure
Gathering evidence of benefits: a structured approach from the JISC Managing Research Data Programme
The work of the Jisc Managing Research Data programme is – along with the rest of the UK higher education sector – taking place in an environment of increasing pressure on research funding. In order to justify the investment made by Jisc in this activity – and to help make the case more widely for the value of investing time and money in research data management – projects and the programme as a whole must be able to clearly express the resultant benefits to the host institutions and to the broader
sector. This paper describes a structured approach to the measurement and description of benefits provided by the work of these projects for the benefit of funders, institutions and researchers. We outline the context of the programme and its work; discuss the drivers and challenges of gathering evidence of benefits; specify benefits as distinct from aims and outputs; present emerging findings and the types of metrics and other evidence which projects have provided; explain the value of gathering evidence in a structured way to demonstrate benefits generated by work in this field; and share lessons learned from progress to date
Dynamics of Epidemics
This article examines how diseases on random networks spread in time. The
disease is described by a probability distribution function for the number of
infected and recovered individuals, and the probability distribution is
described by a generating function. The time development of the disease is
obtained by iterating the generating function. In cases where the disease can
expand to an epidemic, the probability distribution function is the sum of two
parts; one which is static at long times, and another whose mean grows
exponentially. The time development of the mean number of infected individuals
is obtained analytically. When epidemics occur, the probability distributions
are very broad, and the uncertainty in the number of infected individuals at
any given time is typically larger than the mean number of infected
individuals.Comment: 4 pages and 3 figure
Computer programs for estimating civil aircraft economics
Computer programs for calculating airline direct operating cost, indirect operating cost, and return on investment were developed to provide a means for determining commercial aircraft life cycle cost and economic performance. A representative wide body subsonic jet aircraft was evaluated to illustrate use of the programs
Threshold effects for two pathogens spreading on a network
Diseases spread through host populations over the networks of contacts
between individuals, and a number of results about this process have been
derived in recent years by exploiting connections between epidemic processes
and bond percolation on networks. Here we investigate the case of two pathogens
in a single population, which has been the subject of recent interest among
epidemiologists. We demonstrate that two pathogens competing for the same hosts
can both spread through a population only for intermediate values of the bond
occupation probability that lie above the classic epidemic threshold and below
a second higher value, which we call the coexistence threshold, corresponding
to a distinct topological phase transition in networked systems.Comment: 5 pages, 2 figure
Oocyte cryopreservation as an adjunct to the assisted reproductive technologies
The document attached has been archived with permission from the editor of the Medical Journal of Australia. An external link to the publisher’s copy is included. See page 2 of PDF for this item.Keith L Harrison, Michelle T Lane, Jeremy C Osborn, Christine A Kirby, Regan Jeffrey, John H Esler and David Mollo
Random acyclic networks
Directed acyclic graphs are a fundamental class of networks that includes
citation networks, food webs, and family trees, among others. Here we define a
random graph model for directed acyclic graphs and give solutions for a number
of the model's properties, including connection probabilities and component
sizes, as well as a fast algorithm for simulating the model on a computer. We
compare the predictions of the model to a real-world network of citations
between physics papers and find surprisingly good agreement, suggesting that
the structure of the real network may be quite well described by the random
graph.Comment: 4 pages, 2 figure
Assortative mixing in networks
A network is said to show assortative mixing if the nodes in the network that
have many connections tend to be connected to other nodes with many
connections. We define a measure of assortative mixing for networks and use it
to show that social networks are often assortatively mixed, but that
technological and biological networks tend to be disassortative. We propose a
model of an assortative network, which we study both analytically and
numerically. Within the framework of this model we find that assortative
networks tend to percolate more easily than their disassortative counterparts
and that they are also more robust to vertex removal.Comment: 5 pages, 1 table, 1 figur
Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
Many complex networks display a mesoscopic structure with groups of nodes
sharing many links with the other nodes in their group and comparatively few
with nodes of different groups. This feature is known as community structure
and encodes precious information about the organization and the function of the
nodes. Many algorithms have been proposed but it is not yet clear how they
should be tested. Recently we have proposed a general class of undirected and
unweighted benchmark graphs, with heterogenous distributions of node degree and
community size. An increasing attention has been recently devoted to develop
algorithms able to consider the direction and the weight of the links, which
require suitable benchmark graphs for testing. In this paper we extend the
basic ideas behind our previous benchmark to generate directed and weighted
networks with built-in community structure. We also consider the possibility
that nodes belong to more communities, a feature occurring in real systems,
like, e. g., social networks. As a practical application, we show how
modularity optimization performs on our new benchmark.Comment: 9 pages, 13 figures. Final version published in Physical Review E.
The code to create the benchmark graphs can be freely downloaded from
http://santo.fortunato.googlepages.com/inthepress
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