1,325 research outputs found
Nonlocal evolution of weighted scale-free networks
We introduce the notion of globally updating evolution for a class of
weighted networks, in which the weight of a link is characterized by the amount
of data packet transport flowing through it. By noting that the packet
transport over the network is determined nonlocally, this approach can explain
the generic nonlinear scaling between the strength and the degree of a node. We
demonstrate by a simple model that the strength-driven evolution scheme
recently introduced can be generalized to a nonlinear preferential attachment
rule, generating the power-law behaviors in degree and in strength
simultaneously.Comment: 4 pages, 4 figures, final version published in PR
Admission Decision-Making in Hospital Emergency Departments: the Role of the Accompanying Person
In resource-stretched emergency departments, people accompanying patients play key roles in patients' care. This article presents analysis of the ways health professionals and accompanying persons talked about admission decisions and caring roles. The authors used ethnographic case study design involving participant observation and semi-structured interviews with 13 patients, 17 accompanying persons and 26 healthcare professionals in four National Health Service hospitals in south-west England. Focused analysis of interactional data revealed that professionals’ standardization of the patient-carer relationship contrasted with accompanying persons' varied connections with patients. Accompanying persons could directly or obliquely express willingness, ambivalence and resistance to supporting patients’ care. The drive to avoid admissions can lead health professionals to deploy conversational skills to enlist accompanying persons for discharge care without exploring the meanings of their particular relations with patients. Taking a relationship-centered approach could improve attention to accompanying persons as co-producers of healthcare and participants in decision-making
Dynamic scaling regimes of collective decision making
We investigate a social system of agents faced with a binary choice. We
assume there is a correct, or beneficial, outcome of this choice. Furthermore,
we assume agents are influenced by others in making their decision, and that
the agents can obtain information that may guide them towards making a correct
decision. The dynamic model we propose is of nonequilibrium type, converging to
a final decision. We run it on random graphs and scale-free networks. On random
graphs, we find two distinct regions in terms of the "finalizing time" -- the
time until all agents have finalized their decisions. On scale-free networks on
the other hand, there does not seem to be any such distinct scaling regions
Multiscale Analysis of Spreading in a Large Communication Network
In temporal networks, both the topology of the underlying network and the
timings of interaction events can be crucial in determining how some dynamic
process mediated by the network unfolds. We have explored the limiting case of
the speed of spreading in the SI model, set up such that an event between an
infectious and susceptible individual always transmits the infection. The speed
of this process sets an upper bound for the speed of any dynamic process that
is mediated through the interaction events of the network. With the help of
temporal networks derived from large scale time-stamped data on mobile phone
calls, we extend earlier results that point out the slowing-down effects of
burstiness and temporal inhomogeneities. In such networks, links are not
permanently active, but dynamic processes are mediated by recurrent events
taking place on the links at specific points in time. We perform a multi-scale
analysis and pinpoint the importance of the timings of event sequences on
individual links, their correlations with neighboring sequences, and the
temporal pathways taken by the network-scale spreading process. This is
achieved by studying empirically and analytically different characteristic
relay times of links, relevant to the respective scales, and a set of temporal
reference models that allow for removing selected time-domain correlations one
by one
Edge overload breakdown in evolving networks
We investigate growing networks based on Barabasi and Albert's algorithm for
generating scale-free networks, but with edges sensitive to overload breakdown.
the load is defined through edge betweenness centrality. We focus on the
situation where the average number of connections per vertex is, as the number
of vertices, linearly increasing in time. After an initial stage of growth, the
network undergoes avalanching breakdowns to a fragmented state from which it
never recovers. This breakdown is much less violent if the growth is by random
rather than preferential attachment (as defines the Barabasi and Albert model).
We briefly discuss the case where the average number of connections per vertex
is constant. In this case no breakdown avalanches occur. Implications to the
growth of real-world communication networks are discussed.Comment: To appear in Phys. Rev.
Sampling of temporal networks: methods and biases
Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data
Cascade-based attacks on complex networks
We live in a modern world supported by large, complex networks. Examples
range from financial markets to communication and transportation systems. In
many realistic situations the flow of physical quantities in the network, as
characterized by the loads on nodes, is important. We show that for such
networks where loads can redistribute among the nodes, intentional attacks can
lead to a cascade of overload failures, which can in turn cause the entire or a
substantial part of the network to collapse. This is relevant for real-world
networks that possess a highly heterogeneous distribution of loads, such as the
Internet and power grids. We demonstrate that the heterogeneity of these
networks makes them particularly vulnerable to attacks in that a large-scale
cascade may be triggered by disabling a single key node. This brings obvious
concerns on the security of such systems.Comment: 4 pages, 4 figures, Revte
Network analysis of a corpus of undeciphered Indus civilization inscriptions indicates syntactic organization
Archaeological excavations in the sites of the Indus Valley civilization
(2500-1900 BCE) in Pakistan and northwestern India have unearthed a large
number of artifacts with inscriptions made up of hundreds of distinct signs. To
date there is no generally accepted decipherment of these sign sequences and
there have been suggestions that the signs could be non-linguistic. Here we
apply complex network analysis techniques to a database of available Indus
inscriptions, with the aim of detecting patterns indicative of syntactic
organization. Our results show the presence of patterns, e.g., recursive
structures in the segmentation trees of the sequences, that suggest the
existence of a grammar underlying these inscriptions.Comment: 17 pages (includes 4 page appendix containing Indus sign list), 14
figure
Design of Lipid-Based Nanocarriers via Cation Modulation of Ethanol-Interdigitated Lipid Membranes
Short-chain alcohols (i.e., ethanol) can induce membrane interdigitation in saturated-chain phosphatidylcholines (PCs). In this process, alcohol molecules intercalate between phosphate heads, increasing lateral separation and favoring hydrophobic interactions between opposing acyl chains, which interpenetrate forming an interdigitated phase. Unraveling mechanisms underlying the interactions between ethanol and model lipid membranes has implications for cell biology, biochemistry, and for the formulation of lipid-based nanocarriers. However, investigations of ethanol–lipid membrane systems have been carried out in deionized water, which limits their applicability. Here, using a combination of small- and wide-angle X-ray scattering, small-angle neutron scattering, and all-atom molecular dynamics simulations, we analyzed the effect of varying CaCl2 and NaCl concentrations on ethanol-induced interdigitation. We observed that while ethanol addition leads to the interdigitation of bulk phase 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) bilayers in the presence of CaCl2 and NaCl regardless of the salt concentration, the ethanol-induced interdigitation of vesicular DPPC depends on the choice of cation and its concentration. These findings unravel a key role for cations in the ethanol-induced interdigitation of lipid membranes in either bulk phase or vesicular form
Probing empirical contact networks by simulation of spreading dynamics
Disease, opinions, ideas, gossip, etc. all spread on social networks. How
these networks are connected (the network structure) influences the dynamics of
the spreading processes. By investigating these relationships one gains
understanding both of the spreading itself and the structure and function of
the contact network. In this chapter, we will summarize the recent literature
using simulation of spreading processes on top of empirical contact data. We
will mostly focus on disease simulations on temporal proximity networks --
networks recording who is close to whom, at what time -- but also cover other
types of networks and spreading processes. We analyze 29 empirical networks to
illustrate the methods
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