Showing papers on "Network theory published in 2005"

Networks and epidemic models.

Abstract: Networks and the epidemiology of directly transmitted infectious diseases are fundamentally linked. The foundations of epidemiology and early epidemiological models were based on population wide random-mixing, but in practice each individual has a finite set of contacts to whom they can pass infection; the ensemble of all such contacts forms a ‘mixing network’. Knowledge of the structure of the network allows models to compute the epidemic dynamics at the population scale from the individual-level behaviour of infections. Therefore, characteristics of mixing networks—and how these deviate from the random-mixing norm—have become important applied concerns that may enhance the understanding and prediction of epidemic patterns and intervention measures. Here, we review the basis of epidemiological theory (based on random-mixing models) and network theory (based on work from the social sciences and graph theory). We then describe a variety of methods that allow the mixing network, or an approximation to the network, to be ascertained. It is often the case that time and resources limit our ability to accurately find all connections within a network, and hence a generic understanding of the relationship between network structure and disease dynamics is needed. Therefore, we review some of the variety of idealized network types and approximation techniques that have been utilized to elucidate this link. Finally, we look to the future to suggest how the two fields of network theory and epidemiological modelling can deliver an improved understanding of disease dynamics and better public health through effective disease control.

Centrality measures based on current flow

Abstract: We consider variations of two well-known centrality measures, betweenness and closeness, with a different model of information spread. Rather than along shortest paths only, it is assumed that information spreads efficiently like an electrical current. We prove that the current-flow variant of closeness centrality is identical with another known measure, information centrality, and give improved algorithms for computing both measures exactly. Since running times and space requirements are prohibitive for large networks, we also present a randomized approximation scheme for current-flow betweenness.

A network analysis of committees in the U.S. House of Representatives

Abstract: Network theory provides a powerful tool for the representation and analysis of complex systems of interacting agents. Here, we investigate the U.S. House of Representatives network of committees and subcommittees, with committees connected according to “interlocks,” or common membership. Analysis of this network reveals clearly the strong links between different committees, as well as the intrinsic hierarchical structure within the House as a whole. We show that network theory, combined with the analysis of roll-call votes using singular value decomposition, successfully uncovers political and organizational correlations between committees in the House without the need to incorporate other political information.

Models and Methods in Social Network Analysis: Extending Centrality

Abstract: In this chapter, we discuss three ways to extend the basic concept of centrality. The first extends centrality to apply to groups in addition to individual actors. This extension makes it possible to evaluate the relative centrality of different teams or departments within an organization, or to assess whether a particular ethnic minority in a society is more integrated than another. The second extends the concept of centrality to apply to 2mode data in which the data consist of a correspondence between two kinds of nodes, such as individuals and the events they participate in. In the past, researchers have dealt with such data by converting them to standard network data (with considerable loss of information). The extension to 2-mode data means that we can apply the tools and concepts of centrality directly to original 2-mode dataset. The third broadens the centrality concept into a model of the core/periphery structure of a network. With this technique we can evaluate the extent to which a network revolves around a core group of nodes.

The impact of gender, social capital and networks on business ownership: a research agenda

Abstract: Purpose – To advance knowledge about the relationship between small firm networks and social capital by considering the network experiences of women business owners To engage in such research, the paper proposes a conceptual model of business owner networking which is informed by social support theoryDesign/methodology/approach – To develop a conceptual model of business owner networking the paper reviews extant small business network research and argues that, while network theory can provide an understanding of the impact which social capital may have on the entrepreneurial process, a concentration on quantitative methodologies has restricted understanding of this To address the gender bias in small firm network research the paper integrates social support theory into a conceptual model of business owner networksFindings – The conceptual model proposed recognises the interplay between network structures, interactions and contents and argues that consideration of these three network dimensions may pro

governance network theory: towards a second generation

Abstract: Governance networks have gained increasing prominence in the wake of the many reports of government and market failure. Drawing on the burgeoning literature, we first define governance networks and then briefly assess their merits and problems. The key claim is that we are now seeing the development of a second generation of governance network research that focuses on new and yet unanswered questions about the prospects of network-based coordination across different levels of governance: the meta-governance of self-regulating networks; the role of discourse in relation to governance networks, and the democratic problems and potentials of network governance. In answering these important questions we can draw on different theoretical approaches to network governance, and these are briefly delineated.

Dynamical Aspects of Interaction Networks

Abstract: A connection between dynamical systems and network theory is outlined based on a mapping of the dynamics into a discrete probabilistic process, whereby the phase space is partitioned into finite size cells. It is found that the connectivity patterns of networks generated by deterministic systems can be related to the indicators of the dynamics such as local Lyapunov exponents. The procedure is extended to networks generated by stochastic processes.

Multiple networks of public school administrators: An analysis of network content and structure

Abstract: This study examines a public school administrator network from a qualitative paradigm using network theory and methods. Findings identify and describe four distinct networks emerging from administrators’ relationships: the innovation network, the resource network, the social/emotional support network, and the university–school partnership network. Findings suggest that educational leaders do not have just one network, but rather use multiple networks to achieve their organizational objectives. Strategic implications of the structure of these networks and an educational leader’s position within these networks are discussed.

The Web of Law

Abstract: Scientists and mathematicians in recent years have become intensely interested in the structure of networks. Networks turn out to be crucial to understanding everything from physics and biology, to economics and sociology. This article proposes that the science of networks has important contributions to make to the study of law as well. The network of American case law closely resembles the Web in structure and can be studied using techniques that are now being used to describe many other networks, some found in nature, and others created by human action. Studying the legal network can shed light on how the legal system evolves, and many other questions. I present in this article the preliminary results of a significant citation study of nearly four million American legal precedents, which was undertaken at my request by the LexisNexis corporation using the Shepard's citation service. This study demonstrates that the American case law network has the overall structure that network theory predicts it would. It is a highly skewed, scale-free, or similar network. The remarkably great degree of skew is significant. Precendential authority is concentrated in a small number of cases. The vast majority of cases are rarely or never cited. In that it consists largely of dead cases, the Web of Law closely resembles scientific paper citation networks, which consist mostly of dead papers. This article has three parts. First, I introduce some basic concepts of network science, including such important ideas as nodes, links, random graphs, evolving networks, scale-free networks, small worlds, the rich get richer dynamic, node fitness, and clusters. In Part II, I show that both over all and by particular jurisdiction, the Web of Law is a scale-free or similarly highly skewed network. In Part III, I describe some insights that appear from this application and suggest areas for future research. The Web of Law has a structure very similar to that of other real networks, such as the Web and the network of scientific papers. Indeed, preliminary analysis suggests the citation network of U.S. Supreme Court cases is nearly identical to the network of high-energy physics papers, and is well described by a two-power-law model. The Web of Law is organized with hub cases that have many citations and the vast majority of cases, which have very few. The distribution of citation frequency appears to be well described by a two power-law distribution, very similar to scientific paper citation networks. Many promising hypotheses can be generated by considering the law as a scale-free network. State and federal systems can be examined empirically to measure how well integrated each is with itself, and with each other, and how this is changing over time. Legal authorities can be measured to determine whether their authority is emerging or declining. Institutional bodies, such as courts, can be examined in the same way. Clusters of cases, which will reveal the semantic topology of law, can be mapped to determine whether traditional legal categories are accurate or require reform. These methods can be used to develop computer programs to improve the efficiency of searching electronic legal databases. Network theory hints at complex, but analyzable, interactions between the legal doctrines of precedent, and the systems of common law and multiple sovereignties. Because law grows and because it has doctrines of authority, it creates a network of a certain shape, which spontaneously organizes itself. Legal databases, which are huge, precisely documented, and readily accessible, present a perfect opportunity for the application of network science. This research would produce new knowledge of general jurisprudence that has simply been impossible until now, when we have the necessary advances in network science, the fast computers, and the existence of a complete record of the legal network in electronic form, waiting to be explored.

Search in weighted complex networks.

Abstract: We study trade-offs presented by local search algorithms in complex networks which are heterogeneous in edge weights and node degree. We show that search based on a network measure, local betweenness centrality (LBC), utilizes the heterogeneity of both node degrees and edge weights to perform the best in scale-free weighted networks. The search based on LBC is universal and performs well in a large class of complex networks.

Accelerating, hyperaccelerating, and decelerating networks

Abstract: Many growing networks possess accelerating statistics where the number of links added with each new node is an increasing function of network size so the total number of links increases faster than linearly with network size. In particular, biological networks can display a quadratic growth in regulator number with genome size even while remaining sparsely connected. These features are mutually incompatible in standard treatments of network theory which typically require that every new network node possesses at least one connection. To model sparsely connected networks, we generalize existing approaches and add each new node with a probabilistic number of links to generate either accelerating, hyperaccelerating, or even decelerating network statistics in different regimes. Under preferential attachment for example, slowly accelerating networks display stationary scale-free statistics relatively independent of network size while more rapidly accelerating networks display a transition from scale-free to exponential statistics with network growth. Such transitions explain, for instance, the evolutionary record of single-celled organisms which display strict size and complexity limits.

Modeling and control of nonlinear networks : A power-based perspective

Abstract: Increasing demands on efficient power management and conversion, together with demands on reduced harmonic generation, higher bandwidths, and reliability, make it necessary to design devices (e.g., controllers, compensators, filters etc.) that ensure a system to meet certain directives. Such devices are most often developed and studied using linear signal-based approaches. However, since virtually all modern systems are highly complex and inherently nonlinear, linear analysis and design techniques might become insufficient as to ensure certain predefined behaviors, robustness and reliability under all operating conditions, especially if the (controlled or compensated) system is subject to large set-point changes, disturbances, or errors that cause the system to deviate from its nominal point of operation. For that reason, the development of dedicated tools that take the systems nonlinearities into account is of utmost importance. This thesis is concerned with the development of new modeling, analysis and control methods for nonlinear electrical networks. A unified power-based framework that provides a systematic dynamical description of a broad class of networks, including switched-mode power converters, is presented. A major advantage of the method is that the underlying physical structure, like the interconnection of the individual elements, nonlinear phenomena and the power flow, are explicitly incorporated in the model. Taking the network power-flow as a starting point, the concept of passivity is considered from a fairly different point of view with respect to the existing energy-based approaches. The resulting passivity properties are of interest in network theory, but also have applications in control as they suggest a so-called Power-Shaping stabilization method which forms an alternative to the existing method of Energy-Shaping. In addition, useful relations with reactive power are established and lead to the notion of reactive Hamiltonians. In the context of the recently proposed Passivity-Based Control (PBC) strategy for switched-mode power converters, the power-based framework reveals and justifies a revised damping injection scenario that significantly improves the robustness of the closed-loop. Some preliminary steps are taken to extend the power basedmodeling and control approach to mechanical and electro-mechanical systems. The developments throughout the thesis heavily rely on the ideas of R.K. Brayton and J.K. Moser stemming from in the early sixties. Where applicable, the newly obtained results are compared with well-known existing energy-based methods, like the Lagrangian and Hamiltonian approach, and several structural relationships between the methods are established.

Relating network topology to the robustness of centrality measures

Abstract: : This paper reports on a simulation study of social networks that investigated how network topology relates to the robustness of measures of system-level node centrality. This association is important to understand as data collected for social network analysis is often somewhat erroneous and may, to an unknown degree, misrepresent the actual true network. Consequently, the values for measures of centrality calculated from the collected network data may also vary somewhat from those of the true network, possibly leading to incorrect suppositions. To explore the robustness, i.e., sensitivity, of network centrality measures in this circumstance, we conduct Monte Carlo experiments whereby we generate an initial network, perturb its copy with a specific type of error, then compare the centrality measures from two instances. We consider the initial network to represent a true network, while the perturbed represents the observed network. We apply a six-factor full-factorial block design for the overall methodology. We vary several control variables (network topology, size and density, as well as error type, form and level) to generate 10,000 samples each from both the set of all possible networks and possible errors within the parameter space. Results show that the topology of the true network can dramatically affect the robustness profile of the centrality measures. We found that across all permutations that cellular networks had a nearly identical profile to that of uniform-random networks, while the core-periphery networks had a considerably different profile. The centrality measures for the core-periphery networks are highly sensitive to small levels of error, relative to uniform and cellular topologies. Except in the case of adding edges, as the error increases, the robustness level for the 3 topologies deteriorates and ultimately converges.

On the Relationship between Knowledge, Networks, and Local Context

Abstract: In this article, we concentrate on the process of value creation within local business networks We argue that in a local business network the locus of value creation is found within the local context and its ability to create a network of exchange knowledge relations among the various (local) agents In order to build a model that explains the process of knowledge transfer among individuals and organizations in a local business network, we combine the knowledge based view (Kogut and Zander, 1992; Conner and Prahalad, 1996; Foss, 1996a, 1996b) and the network perspective (Thorelli, 1986; Jarrillo, 1988; Powell, 1990; Uzzi, 1997; Gulati, 1999; Gulati et al, 2000) The knowledge-based view (KBV) within the framework of the resource-based approach is concerned with firm-level analysis; the network approach literature is far more descriptive and seeks to understand how networks affect competition (Nielsen, 2000) It appears that these two approaches are diametrically opposed: the knowledge-based view is exclusively engaged with analysis of the individual firm’s knowledge creation and accumulation, and has nothing to say about inter-firm relations; the primary research interest of the network perspective is, on the other hand, to identify, categorize and theorize relations between firms (networks) We argue that it is indeed feasible for knowledge researchers to draw in a fruitful way on network insights (and vice versa) (Foss, 1999; Dagnino, 1999; Nielsen, 2000) More specifically, the idea of the local business network provides one possible bridge between the two approaches We will first present a critical review of the knowledge-based theory, which provides an outline of the main theoretical perspectives within this field of research about knowledge transfer, and a review of the network theory approach, which analyses the importance of the relations among different organizations by focusing on the process of sharing and transferring knowledge A description of our conceptual model, which explains how local context influences the performance of firms belonging to a local business network, follows Finally, the conclusions and implications for firms and research are discussed

Modeling and analysis of massive social networks

Abstract: Traditional epidemiological research has focused on rate-based differential-equation models with completely mixing populations [6, 7, 44]. Although successful in explaining certain phenomena of disease spreading, the traditional approach is unable to deal with disease spreading in realistic massive social networks, where most people only mix locally with few other people. We have develop an approach based on a combination of network theory and discrete-event simulations to study epidemics in large urban areas ; which do not assume complete mixing populations. Our results include (1) detailed structural and temporal analyses of the social contact networks produced by TRANSIMS [10], a simulator for detailed transportation/traffic studies; (2) realistic simulation of contagious diseases (e.g., smallpox) on the social contact networks through EpiSims [32], a simulation-based analytical tool to study the spread of infectious diseases in an urban environment; (3) identifying a number of new measures that are significant for understanding epidemics and for developing new strategies in policy planning; (4) introduction of random graph models for theoretical analysis of the structural and algorithmic aspects of the social networks; and (5) combinatorial formulations and approximation algorithms for performing quarantine, vaccination and sensor placement, as aids to decision-making. The social network that we have mostly dealt with is for the city of Portland; Oregon, USA, developed as a part of the TRANSIMS/EpiSims project at the Los Alamos National Laboratory. The most expressive social contact network is a bipartite graph representing people and locations; edges represent people visiting locations on a typical day. We also build random graph models to generate a family of social networks by taking as input some basic parameters of the Portland social network, and analyze social networks generated by these models.

Mapping WordNet to a relational network

Abstract: This paper discusses the implementation of a lexical knowledge base, based on a conceptualization of the problem domain in terms of relational network notation. In particular, we focus here on the importing of lemmas as instances from WordNet 2.0 into the knowledge base. Relational network notation (RNN) offers a simple yet powerful means for representing lexicogrammatical, semantic and sememic information. RNN is driven by relational network theory and incorporates developments in the theory which have been shown to not only describe but also explain linguistic phenomena in a neurologically plausible manner.

A Case Study of Foreign Direct Investment by Taiwanese Firms and Migration of Industrial Network

Abstract: Along with the concept of network theory, this research describes the interaction among members of the industrial network and the modes of foreign direct investment. Eight cases were selected from the list of names provided by the Corporate Synergy Development Center that represents the paradigm of industrial networks in Taiwan. The results of the study show three types of network migration: Market, Hierarchy, and Hybrid. Proposed are three kinds of network strategies: Proactive, Analyzer, and Defender. From the research, it is clear that (1) the relationships within the domestic industrial network will further impact the industrial network migration model; and (2) in order to cope with the transformation of industrial network, a firm with a different network position will adopt a different internationalization network strategy. Implications for managers are that firms need to adopt various strategies according to particular situations of the industrial network.

Complexity of Business Network Relationships

Abstract: This paper aims to engage the relational perspective in network theory into analysis of the dynamics of networking. After reviewing a number of concepts that map the emergence and evolution of business network relationships, the paper proposes a model with the leading relational dimensions that frame transactions and exchanges in business networks. The evolution of business network relationships is explained with decisions and choices made by individual actors in a context of resource constraints and opportunities. The paper distinguishes between human and non-human actors and we accept objects (technologies and cultural artifacts) as actors in heterogeneous networks. Each relationship is framed by eleven leading relational dimensions or relational properties that can be used for comparisons across dyadic business relationships, or for projecting relational outcomes in a network context. Finally we employ an extended framework of the Actors-Resources-Activities model by Hakansson & Johanson (1993) to explain the evolution and dynamics of the business relationship through interconnected activities and resource flows, where overlapping interests of business actors produce shared resource flows and network activities. This model demonstrates the multiplexity of network relationships and the need for multi-level analysis of business networks.

The Impact of Gender, Social Capital and Networks on Business Ownership: A Research Agenda

Abstract: Purpose – To advance knowledge about therelationship between small firm networks and social capital by considering thenetwork experiences of women business owners. To engage in such research, thepaper proposes a conceptual model of business owner networking which isinformed by social support theory. Design/methodology/approach – To develop a conceptual model of businessowner networking the paper reviews extant small business network research andargues that, while network theory can provide an understanding of the impactwhich social capital may have on the entrepreneurial process, a concentrationon quantitative methodologies has restricted understanding of this. To addressthe gender bias in small firm network research the paper integrates socialsupport theory into a conceptual model of business owner networks. Findings – The conceptual model proposed recognises the interplay betweennetwork structures, interactions and contents and argues that consideration ofthese three network dimensions may provide insights into the impact of genderon business owner networks, social capital and experiences of businessownership. The paper also discusses the methodological implications of thismodel and proposes a research agenda for future business owner networkresearch. Originality/value – The paper addresses a recognised gap in extant smallbusiness network research and proposes a conceptual model of business ownernetworking which may be better suited to and more reflective of women businessowners' networking experiences. (Publication abstract)

A computational framework in modeling cellular communication in sepsis

Abstract: The paper proposes a model that relieves the characteristics of the patient evolution in SEPSIS as the result of correct or corrupted information transfer as the cell level, using for this aim methods which are specific for studying complex systems: nonlinear dynamics, statistical methods, data transmission and network theory. We place particular emphasis on network theory and its importance in augmenting the framework for the quantitative study of complex systems. Specifically, we discuss issues, arising from network theory, in our understanding the structure of cellular signaling networks involved in SEPSIS phenomena. Finally, we discuss the possibility to implement two simulation mechanisms, one based on autonomous (multi)agent blackboard architecture, for modeling intracellular communication, the other, based on small-world or even scale-free networks, for modeling intercellular communication.

A Study on the Interactive Mechanism for Relational Network and Entrepreneurship

Abstract: With privately owned high-technology and newly established enterprises as the research objects,it expounds the rationalization of using the network theory on the base of reviewing the previous research on entrepreneurship and in the combination of analyzing the Chinese culture and the transition socioeconomic background Then it presents a conceptual framework of the interactive mechanism for relational network and entrepreneurship,and discusses it in detail by explaining the value of network in the different stage of entrepreneur and the evolution law of entrepreneurial network Finally it probes into the problem of how to construct and manage the entrepreneurial network at the levels of entrepreneur,government and society

Node Exchange Network and its Statistical Analysis

Abstract: In considering a social network, there are cases where people is transferred to another place. Then the physical (direct) relations among nodes are lost by the movement. In terms of a network theory, some nodes break the present connections with neighboring nodes, move and there build new connections of nodes. For simplicity we here consider only that two nodes exchange the place each other on a network. Such exchange is assumed to be constantly carried out. We study this dynamic network (node exchange network NEN) and uncover some new features which usual networks do not contain. We mainly consider average path length and the diameter. Lastly we consider a propagation of one virus on the network by a computer simulation. They are compared to other networks investigated hitherto. The relation to a scale free network is also discussed.

Evaluation of business networks in the AusIndustry business network program

Abstract: Researchers have found that inter-firm collaboration, that is, co-operative business networks, can provide a competitive advantage that would not be possible independently for small sized firms Work has been done by some governments, for example, Danish, Norwegian, New Zealand, American and Japanese, in the area of policy and practice of business networks because they have realised the importance of business networking and have encouraged collaboration of small firms by assisting in the facilitation of networks The Australian government established a Business Network Program which ran for four years and several studies were completed on various aspects of the program during that period However, there had been no particular research that examined the success or other outcomes of these networks, thus providing the basis for the research question addressed in this research: How and why did the business networks developed in the AusIndustry Business Networks Program, succeed or not succeed? Further, questions relating to how and why these outcomes may have occurred or how they may have been measured in the Australian government facilitated program were also unanswered A review of the extant literature in this area established the theoretical foundations upon which this research is based and made possible the development of a model comprising three constructs or research issues that would address the research question: RI 1: How and why is network success evaluated? RI 2: How and why do the internal and external environments affect the outcomes of the network? RI 3: How does facilitation affect the network? In order to address these research issues and the research question, a protocol was developed and case study interviews with the lead business of sixteen networks participating in the AusIndustry Business Network Program were carried out The resultant data was compared for each of the research issues through a qualitative methodology from which conclusions and answers to the research question and issues were derived The results of this research showed that network members evaluated their own outcomes often using multiple measures, both qualitative and quantitative, with the most common criteria being whether the network continued or discontinued Moreover, it was concluded in this research that successful networks usually had a single goal or purpose for joining a network which they ultimately achieved In contrast the unsuccessful networks generally joined the network with multiple goals and which were not all achieved, thus contributing to their lack of success This result was not evident in the literature reviewed in chapter 2 Additionally, the findings showed that high levels of trust, commitment and reciprocity were essential elements in the success of business networks More importantly this study found that whilst all successful networks had these elements, some of the non successful ones also reported high levels of trust, commitment and reciprocity Thus it appeared in this study that whilst these elements are important for network success, they do not alone ensure that success, further, it was noted that for any network that reported a lack of any one of these elements, non success was more likely In relation to this finding was the discovery that in these networks formal contracts between the network members increased the levels of commitment and reciprocity and thus increased the chances of success When external environmental factors were examined in relation to their impact on network success, it was found that whilst all had some impact on their business generally, competition was noted as having the highest impact and government or legal issues the lowest impact Finally, this research found that facilitation did not necessarily contribute to a network’s success but that possible a lack of appropriate facilitation style did contribute to the non-success of networks However, it was clear that the small networks needed less facilitator guidance overall and that the larger networks definitely needed facilitation and guidance Moreover, it was found that the type of facilitation at the various stages of the network process were more important to the likelihood of success, rather than the mere presence of a facilitator Thus, the main contribution of this theory building research is to extend the general level of knowledge about business networks and provide new insights into network theory and the value of networks using an original application of existing knowledge This knowledge can contribute to network education and training in business schools and can contribute to the development of future government policy and practice pertaining to network programs

Connecting the dots to disconnect them: a study into network evolution and dynamics for analyzing terrorist networks

Abstract: For a long time, a lack of sucient data has been an obstacle to the intelligence community. This study providesan integrated approach which combines network theory and data mining to analyze 1440 instances of terrorismthat occurred up to 2002. The study reveals interesting patterns on the evolution of these terrorist organizationsover two decades.Keywords: Data mining, Network dynamics, Network evolution, Network mining 1. INTRODUCTION Data mining 1 is the process of discovering novel patterns in databases. Traditional data mining techniques -such as machine learning - assume that the attributes are independent and that the values that each of theseattributes can take are also independent. This assumption made theoretical analysis of data mining techniquesfeasible, but unfortunately it is an unrealistic assumption in many real life situations. The second consequenceof this assumption is that most techniques require a considerable amount of data to reach any useful conclusions.Relaxing the inaccurate assumption that the attributes are independent and the values that each attributecan take are also independent would naturally mean that there are virtual links connecting these attributes andtheir values. As such, one can visualize the relationship between these attributes and their values as a network.This is a much richer representation that can be used to substantiate data analysis with some of the rigorousmeasures used in social network theory. This approach we call network mining.In network mining, a number of variations exist in the literature. One is known as link analysis (see forexample