Showing papers in "Network Science in 2010"

The Structure of Financial Networks

Abstract: We present here an overview of the use of networks in Finance and Economics. We show how this approach enables us to address important questions as, for example, the structure of control chains in financial systems, the systemic risk associated with them and the evolution of trade between nations. All these results are new in the field and allow for a better understanding and modelling of different economic systems.

Networks in urban design. six years of research in multiple centrality assessment

Abstract: Multiple Centrality Assessment (MCA) is a methodology of mapping centrality in cities that applies knowledge of complex network analysis to networks of urban streets and intersections. This methodology emerged from research initiated some six years ago at Polytechnic of Milan, Italy, and now continuing at University of Strathclyde, Glasgow, through a close partnership and collaboration between scholars in urban planning and design and in the physics of complex networks. After six years and many publications, it is probably time for us to make a point on what has been achieved and what remains to be achieved in the future. As most of the whole research has already been published, we forward the reader to those publications for more detailed information. The scope of this paper is to provide the overall sense of this experience so far and a road-map to its main results.

Resistance Distance, Information Centrality, Node Vulnerability and Vibrations in Complex Networks

Abstract: We discuss three seemingly unrelated quantities that have been intro- duced in different fields of science for complex networks. The three quantities are the resistance distance, the information centrality and the node displacement. We first prove various relations among them. Then we focus on the node displacement, showing its usefulness as an index of node vulnerability. We argue that the node dis- placement has a better resolution as a measure of node vulnerability than the degree and the information centrality.

Networks and Models with Heterogeneous Population Structure in Epidemiology

Abstract: Heterogeneous population structure can have a profound effect on infectious disease dynamics, and is particularly important when investigating “tactical” disease control questions. At times, the nature of the network involved in the transmission of the pathogen (bacteria, virus, macro-parasite, etc.) appears to be clear; however, the nature of the network involved is dependent on the scale (e.g. within-host, between-host, or between-population), the nature of the contact, which ranges from the highly specific (e.g. sexual acts or needle sharing at the person-to-person level) to almost completely non-specific (e.g. aerosol transmission, often over long distances as can occur with the highly infectious livestock pathogen foot-and-mouth disease virus—FMDv—at the farm-to-farm level, e.g. Schley et al. in J. R. Soc. Interface 6:455–462, 2008), and the timescale of interest (e.g. at the scale of the individual, the typical infectious period of the host). Theoretical approaches to examining the implications of particular network structures on disease transmission have provided critical insight; however, a greater challenge is the integration of network approaches with data on real population structures. In this chapter, some concepts in disease modelling will be introduced, the relevance of selected network phenomena discussed, and then results from real data and their relationship to network analyses summarised. These include examinations of the patterns of air traffic and its relation to the spread of SARS in 2003 (Colizza et al. in BMC Med., 2007; Hufnagel et al. in Proc. Natl. Acad. Sci. USA 101:15124–15129, 2004), the use of the extensively documented Great Britain livestock movements network (Green et al. in J. Theor. Biol. 239:289–297, 2008; Robinson et al. in J. R. Soc. Interface 4:669–674, 2007; Vernon and Keeling in Proc. R. Soc. Lond. B, Biol. Sci. 276:469–476, 2009) and the growing interest in combining contact structure data with phylogenetics to identify real contact patterns as they directly relate to diseases of interest (Cottam et al. in PLoS Pathogens 4:1000050, 2007; Hughes et al. in PLoS Pathogens 5:1000590, 2009).

A Hierarchy of Networks Spanning from Individual Organisms to Ecological Landscapes

Abstract: Living systems are hierarchically organised. A number of components are linked by the multiplicity of interactions at each level (from organisms to species to ecosystems). This kind of compositional and hierarchical complexity is a computational and conceptual challenge. We need new approaches to determine the key components of large interaction networks and we need to better understand how they influence the system dynamics horizontally (at the same level) and vertically (between organisational levels). We provide examples for various interaction networks (animal social group, food web, landscape) and discuss how to dynamically link them.

Dynamics of Networks of Leaky-Integrate-and-Fire Neurons

Abstract: The dynamics of pulse-coupled leaky-integrate-and-fire neurons is discussed in networks with arbitrary structure and in the presence of delayed interactions The evolution equations are formally recasted as an event-driven map in a general context where the pulses are assumed to have a finite width The final structure of the mathematical model is simple enough to allow for an easy implementation of standard nonlinear dynamics tools We also discuss the properties of the transient dynamics in the presence of quenched disorder (and δ-like pulses) We find that the length of the transient depends strongly on the number N of neurons It can be as long as 106–107 inter-spike intervals for relatively small networks, but it decreases upon increasing N because of the presence of stable clustered states Finally, we discuss the same problem in the presence of randomly fluctuating synaptic connections (annealed disorder) The stationary state turns out to be strongly affected by finite-size corrections, to the extent that the number of clusters depends on the network size even for N≈20,000

NESSIE: Network Example Source Supporting Innovative Experimentation

Abstract: We describe a new web-based facility that makes available some realistic examples of complex networks. NESSIE (Network Example Source Supporting Innovative Experimentation) currently contains 12 specific networks from a diverse range of application areas, with a Scottish emphasis. This collection of data sets is designed to be useful for researchers in network science who wish to evaluate new algorithms, concepts and models. The data sets are available to download in two formats (MATLAB’s .mat format and .txt files readable by packages such as Pajek), and some basic MATLAB tools for computing summary statistics are also provided.

Complex Networks: An Invitation

Abstract: Most of us recognize that connections are important. The science of connectivity has formalized and quantified this broad truism and produced a collection of concepts and tools that have proved to be remarkably useful in practice. With this brief opening chapter, we aim to prepare the reader for the cutting-edge and application-specific material to be found in the rest of the book by providing some motivation and background material. We also hope to give a taste of the excitement and the challenges that this area has to offer.

Dynamics and Statistics of Extreme Events

Abstract: Complex dynamics is characterized by an irregular, non-periodic time dependence of characteristic quantities. Rare fluctuations which lead to unexpectedly large (or small) values are called extreme events. Since such large deviations from the system’s mean behavior have in many applications huge impact, their statistical characterization and their dynamical origin are of relevance. We discuss recent approaches, with special emphasis on dynamics on networks.

Revealing Structure of Complex Biological Systems Using Bayesian Networks

Abstract: Bayesian networks represent statistical dependencies among variables; they are able to model multiple types of relationships, including stochastic, non-linear, and arbitrary combinatoric. Such flexibility has made them excellent models for reverse-engineering structure of complex networks. This chapter reviews the use of Bayesian networks for probing structure of biological systems. We begin with an introduction to Bayesian networks, addressing especially issues of their interpretation as relates to understanding system structure. We then cover how Bayesian network structures are learned from data, considering a popular scoring metric, the BDe, in detail. We finish by reviewing the uses of Bayesian networks in biological systems to date and the concurrent advances in Bayesian network methodology tailored for use in biology.

From Topology to Phenotype in Protein–Protein Interaction Networks

Abstract: We have recently witnessed an explosion in biological network data along with the development of computational approaches for their analyses. This new interdisciplinary research area is an integral part of systems biology, promising to provide new insights into organizational principles of life, as well as into evolution and disease. However, there is a danger that the area might become hindered by several emerging issues. In particular, there is typically a weak link between biological and computational scientists, resulting in the use of simple computational techniques of limited potential to explain these complex biological data. Hence, there is a danger that the community might view the topological features of network data as mere statistics, ignoring the value of the information contained in these data. This might result in the imposition of scientific doctrines, such as scale-free-centric (on the modelling side) and genome-centric (on the biological side) opinions onto this nascent research area. In this chapter, we take a network science perspective and present a brief, high-level overview of the area, commenting on possible challenges ahead. We focus on protein–protein interaction networks (PINs) in which nodes correspond to proteins in a cell and edges to physical bindings between the proteins.