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.

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

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

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