2,198 research outputs found
Structure and Response in the World Trade Network
We examine how the structure of the world trade network has been shaped by
globalization and recessions over the last 40 years. We show that by treating
the world trade network as an evolving system, theory predicts the trade
network is more sensitive to evolutionary shocks and recovers more slowly from
them now than it did 40 years ago, due to structural changes in the world trade
network induced by globalization. We also show that recession-induced change to
the world trade network leads to an \emph{increased} hierarchical structure of
the global trade network for a few years after the recession.Comment: 4 pages, 4 figures, to appear in Phys. Rev. Let
Statistical Mechanics of Community Detection
Starting from a general \textit{ansatz}, we show how community detection can
be interpreted as finding the ground state of an infinite range spin glass. Our
approach applies to weighted and directed networks alike. It contains the
\textit{at hoc} introduced quality function from \cite{ReichardtPRL} and the
modularity as defined by Newman and Girvan \cite{Girvan03} as special
cases. The community structure of the network is interpreted as the spin
configuration that minimizes the energy of the spin glass with the spin states
being the community indices. We elucidate the properties of the ground state
configuration to give a concise definition of communities as cohesive subgroups
in networks that is adaptive to the specific class of network under study.
Further we show, how hierarchies and overlap in the community structure can be
detected. Computationally effective local update rules for optimization
procedures to find the ground state are given. We show how the \textit{ansatz}
may be used to discover the community around a given node without detecting all
communities in the full network and we give benchmarks for the performance of
this extension. Finally, we give expectation values for the modularity of
random graphs, which can be used in the assessment of statistical significance
of community structure
Fast algorithm for detecting community structure in networks
It has been found that many networks display community structure -- groups of
vertices within which connections are dense but between which they are sparser
-- and highly sensitive computer algorithms have in recent years been developed
for detecting such structure. These algorithms however are computationally
demanding, which limits their application to small networks. Here we describe a
new algorithm which gives excellent results when tested on both
computer-generated and real-world networks and is much faster, typically
thousands of times faster than previous algorithms. We give several example
applications, including one to a collaboration network of more than 50000
physicists.Comment: 5 pages, 4 figure
Planning and developing a web-based intervention for active surveillance in prostate cancer: an integrated self-care programme for managing psychological distress
Objectives: To outline the planning, development and optimisation of a psycho-educational behavioural intervention for patients on active surveillance for prostate cancer. The intervention aimed to support men manage active surveillance-related psychological distress. / Methods: The person-based approach (PBA) was used as the overarching guiding methodological framework for intervention development. Evidence-based methods were incorporated to improve robustness. The process commenced with data gathering activities comprising the following four components: âą A systematic review and meta-analysis of depression and anxiety in prostate cancer âą A cross-sectional survey on depression and anxiety in active surveillance âą A review of existing interventions in the field âą A qualitative study with the target audience The purpose of this paper is to bring these components together and describe how they facilitated the establishment of key guiding principles and a logic model, which underpinned the first draft of the intervention. / Results: The prototype intervention, named PROACTIVE, consists of six Internet-based sessions run concurrently with three group support sessions. The sessions cover the following topics: lifestyle (diet and exercise), relaxation and resilience techniques, talking to friends and family, thoughts and feelings, daily life (money and work) and information about prostate cancer and active surveillance. The resulting intervention has been trialled in a feasibility study, the results of which are published elsewhere. / Conclusions: The planning and development process is key to successful delivery of an appropriate, accessible and acceptable intervention. The PBA strengthened the intervention by drawing on target-user experiences to maximise acceptability and user engagement. This meticulous description in a clinical setting using this rigorous but flexible method is a useful demonstration for others developing similar interventions. / Trial registration and Ethical Approval: ISRCTN registered: ISRCTN38893965. NRES Committee South Central â Oxford A. REC reference: 11/SC/0355
Weighted network modules
The inclusion of link weights into the analysis of network properties allows
a deeper insight into the (often overlapping) modular structure of real-world
webs. We introduce a clustering algorithm (CPMw, Clique Percolation Method with
weights) for weighted networks based on the concept of percolating k-cliques
with high enough intensity. The algorithm allows overlaps between the modules.
First, we give detailed analytical and numerical results about the critical
point of weighted k-clique percolation on (weighted) Erdos-Renyi graphs. Then,
for a scientist collaboration web and a stock correlation graph we compute
three-link weight correlations and with the CPMw the weighted modules. After
reshuffling link weights in both networks and computing the same quantities for
the randomised control graphs as well, we show that groups of 3 or more strong
links prefer to cluster together in both original graphs.Comment: 19 pages, 7 figure
Directed network modules
A search technique locating network modules, i.e., internally densely
connected groups of nodes in directed networks is introduced by extending the
Clique Percolation Method originally proposed for undirected networks. After
giving a suitable definition for directed modules we investigate their
percolation transition in the Erdos-Renyi graph both analytically and
numerically. We also analyse four real-world directed networks, including
Google's own webpages, an email network, a word association graph and the
transcriptional regulatory network of the yeast Saccharomyces cerevisiae. The
obtained directed modules are validated by additional information available for
the nodes. We find that directed modules of real-world graphs inherently
overlap and the investigated networks can be classified into two major groups
in terms of the overlaps between the modules. Accordingly, in the
word-association network and among Google's webpages the overlaps are likely to
contain in-hubs, whereas the modules in the email and transcriptional
regulatory networks tend to overlap via out-hubs.Comment: 21 pages, 10 figures, version 2: added two paragaph
Preferential attachment of communities: the same principle, but a higher level
The graph of communities is a network emerging above the level of individual
nodes in the hierarchical organisation of a complex system. In this graph the
nodes correspond to communities (highly interconnected subgraphs, also called
modules or clusters), and the links refer to members shared by two communities.
Our analysis indicates that the development of this modular structure is driven
by preferential attachment, in complete analogy with the growth of the
underlying network of nodes. We study how the links between communities are
born in a growing co-authorship network, and introduce a simple model for the
dynamics of overlapping communities.Comment: 7 pages, 3 figure
Correlation, Network and Multifractal Analysis of Global Financial Indices
We apply RMT, Network and MF-DFA methods to investigate correlation, network
and multifractal properties of 20 global financial indices. We compare results
before and during the financial crisis of 2008 respectively. We find that the
network method gives more useful information about the formation of clusters as
compared to results obtained from eigenvectors corresponding to second largest
eigenvalue and these sectors are formed on the basis of geographical location
of indices. At threshold 0.6, indices corresponding to Americas, Europe and
Asia/Pacific disconnect and form different clusters before the crisis but
during the crisis, indices corresponding to Americas and Europe are combined
together to form a cluster while the Asia/Pacific indices forms another
cluster. By further increasing the value of threshold to 0.9, European
countries France, Germany and UK constitute the most tightly linked markets. We
study multifractal properties of global financial indices and find that
financial indices corresponding to Americas and Europe almost lie in the same
range of degree of multifractality as compared to other indices. India, South
Korea, Hong Kong are found to be near the degree of multifractality of indices
corresponding to Americas and Europe. A large variation in the degree of
multifractality in Egypt, Indonesia, Malaysia, Taiwan and Singapore may be a
reason that when we increase the threshold in financial network these countries
first start getting disconnected at low threshold from the correlation network
of financial indices. We fit Binomial Multifractal Model (BMFM) to these
financial markets.Comment: 32 pages, 25 figures, 1 tabl
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