Showing papers on "Network theory published in 2007"
TL;DR: This review identifies several broad research areas where the networks approach could greatly enhance the understanding of social patterns and processes in animals.
Abstract: Social network theory has made major contributions to our understanding of human social organisation but has found relatively little application in the field of animal behaviour. In this review, we identify several broad research areas where the networks approach could greatly enhance our understanding of social patterns and processes in animals. The network theory provides a quantitative framework that can be used to characterise social structure both at the level of the individual and the population. These novel quantitative variables may provide a new tool in addressing key questions in behavioural ecology particularly in relation to the evolution of social organisation and the impact of social structure on evolutionary processes. For example, network measures could be used to compare social networks of different species or populations making full use of the comparative approach. However, the networks approach can in principle go beyond identifying structural patterns and also can help with the understanding of processes within animal populations such as disease transmission and information transfer. Finally, understanding the pattern of interactions in the network (i.e. who is connected to whom) can also shed some light on the evolution of behavioural strategies.
427 citations
TL;DR: A survey of the use of graph theoretical techniques in biology is presented in this article, with an emphasis on synchronisation and disease propagation, as well as the link between structural network properties and dynamics.
Abstract: A survey of the use of graph theoretical techniques in Biology is presented. In particular, recent work on identifying and modelling the structure of bio-molecular networks is discussed, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronisation and disease propagation.
415 citations
TL;DR: In this article, a new class of measures of structural centrality for networks is introduced, called delta centralities, which is based on the concept of efficient propagation of information over the network.
Abstract: We introduce delta centralities, a new class of measures of structural centrality for networks. In particular, we focus on a measure in this class, the information centrality C I , which is based on the concept of efficient propagation of information over the network. C I is defined for both valued and non-valued graphs, and applies to groups as well as individuals. The measure is illustrated and compared with respect to the standard centrality measures by using a classic network data set. The statistical distribution of information centrality is investigated by considering large computer generated graphs and two networks from the real world.
374 citations
TL;DR: An experimental study of the quality of centrality scores estimated from a limited number of SSSP computations under various selection strategies for the source vertices is presented.
Abstract: Centrality indices are an essential concept in network analysis. For those based on shortest-path distances the computation is at least quadratic in the number of nodes, since it usually involves solving the single-source shortest-paths (SSSP) problem from every node. Therefore, exact computation is infeasible for many large networks of interest today. Centrality scores can be estimated, however, from a limited number of SSSP computations. We present results from an experimental study of the quality of such estimates under various selection strategies for the source vertices.
367 citations
TL;DR: In this paper, the role of network resources and associated mechanisms that affect the timing of entry into an emerging product market were examined, and the authors discussed the implications of their findings for research on social networks and market entry.
Abstract: This paper focuses on the role of network resources and examines the associated mechanisms that affect the timing of entry into an emerging product market. Linking network theory to market entry research, I analyze the pattern in the structure, relation, and composition of 517 firms' strategic alliances as the firms face the decision of whether and when to enter the networking switches market over a 13-year period from 1989 to 2001. The context for empirical testing is the voice/data convergence between telephony communications and computer networking technologies during which the industry boundary blurs. Firms that have access to information of high quality, large quantity, and compositional heterogeneity are likely to enter the newly developed market more quickly. However, network configuration lock-in and network costs may counterbalance the benefits derived from network resources. I discuss the implications of these findings for research on social networks and the timing of market entry. Copyright © 2007 John Wiley & Sons, Ltd.
177 citations
TL;DR: In this paper, the authors advocate that case study research needs to renew itself and employ its full potential as an innovative theory-generating methodology in management disciplines; and propose that a viable strategy for such renewal is to exploit the power of case-study research and network theory as supplementary methodologies.
Abstract: Purpose – The purpose of this paper is to advocate that case study research needs to renew itself and employ its full potential as an innovative theory‐generating methodology in management disciplines; and to propose that a viable strategy for such renewal is to exploit the power of case study research and network theory as supplementary methodologies.Design/methodology/approach – The paper is a reflective and synthesising comparative study.Findings – If one steps down from the tip of the iceberg and inspects the underwater properties of case study research and network theory a common core is found: the recognition of complexity. The methodologies supplement each other, case study research primarily using verbal language and qualitative data, while network theory uses a nodes‐and‐links language that opens up for verbal, graphic and mathematical treatment. Case study research is primarily associated with qualitative research in social sciences and network theory with quantitative research in both social an...
135 citations
TL;DR: Preliminary results show that certain environmental and firm-level factors may impact the eventual evolution of such structures and this methodology allows the spontaneous generation of network structures so that it is possible to examine the potential factors behind the evolution of different SN topologies.
Abstract: Supply chains, or supply networks (SNs), exist in a multitude of different topologies, yet little is known concerning how such topologies grow, evolve, and adapt over time. To study this complex phenomenon, we begin by identifying some primary topological structures that SNs may form. Then, to investigate the evolution of such structures, a theory-based framework is developed that combines aspects of complex adaptive systems theory, industrial growth theory, network theory, market structure, and game theory. This framework specifies categories of rules that may evoke different behaviors in the two fundamental components of any adaptive SN, i.e., the environment and the Arms in that environment. The framework is implemented as a multiparadigm simulation utilizing software agents and it joins discrete-time with discrete-event simulation formalisms. This methodology allows the spontaneous generation of network structures so that it is possible to examine the potential factors behind the evolution of different SN topologies. Using data and parameters extracted from 80 years of the U.S. automobile industry, we have been able to "grow" a wide range of SN topologies and preliminary results show that certain environmental and firm-level factors may impact the eventual evolution of such structures.
86 citations
TL;DR: The theoretical results and the modelling show that single-point measurements for the localization of distortion-producing sources are not practical when these sources are distributed over the network.
Abstract: This paper extends the underlying network theory defining the direction of harmonic power in single-phase systems to three-phase systems. By employing symmetrical component theory for multi-frequency systems, zero-, positive- and negative-phase sequence components may be considered separately. However, the theoretical results and the modelling show that single-point measurements for the localization of distortion-producing sources are not practical when these sources are distributed over the network.
85 citations
TL;DR: The study of organizational networks has a long history in the social and behavioral sciences as discussed by the authors, and there are several substantive theories of organizational behavior but rarely do they alone explain outcomes, since almost all effects are contingent upon context.
Abstract: The study of organizational networks has a long history in the social and behavioural sciences. On the micro side, anthropologists and psychologists studied interpersonal networks within organizations (e.g., Roethlisberger and Dickson, 1939). On the macro side, sociologists studied interlocking directorates (Allen, 1974; Levine, 1972), human service delivery systems (Aldrich, 1976; Rogers, 1974) and community power structures (Hunter, 1953; Laumann and Pappi, 1976; Perrucci and Pilisuk, 1970; Turk, 1977). Although management scholars had studied human service networks as well (e.g., Van de Ven, 1976), most credit Tichy et al. (1979) with introducing the topic of network analysis to the management literature. The institutionalization of the network approach in management circles is evident in the Academy of Management's 2002 meeting theme, 'Building Effective Networks' and special issues devoted to network analysis in the Academy of Management Journal (volume 47, issue 6, December 2004) and the Academy of Management Review (volume 31, issue, 3 July 2006). After all these years and hundreds of publications I think it is fair to ask: has a network perspective on organizational behaviour lived up to its promises? To spare you the suspense, my answer is yes — but we are not yet at the point where we have a single omnibus network theory of organizational behaviour or anything approaching universal laws of network organizations. Rather, as this essay will show, networks are key elements in several substantive theories of organizational behavior but rarely do they alone explain outcomes. Almost all effects are contingent upon context. On the empirical side, the evidence is fairly convincing that networks matter, i.e., network variables explain significant amounts of variance, but often research designs leave much to be desired, much of the work is descriptive, and we are not necessarily sure why or how networks matter. In this way, the views expressed in this essay are not unsympathetic to those expressed by Salancik (1995) in his classic critique of network theories of organization. The essay will be divided into four parts. First, I describe some of the ways that network analysis is more complicated than standard survey analysis. Secondly, I address the question: what role do network ideas play in network theories of organizational behaviour? Thirdly, I evaluate network theories and research
84 citations
TL;DR: An entropy-based measure of centrality appropriate for traffic that propagates by transfer and flows along paths is proposed and can be applied to most network types, whether binary or weighted, directed or undirected, connected or disconnected.
81 citations
TL;DR: A method for rapid computation of group betweenness centrality whose running time (after preprocessing) does not depend on network size and may assist in finding further properties of complex networks and may open a wide range of research opportunities.
Abstract: In this paper, we propose a method for rapid computation of group betweenness centrality whose running time (after preprocessing) does not depend on network size. The calculation of group betweenness centrality is computationally demanding and, therefore, it is not suitable for applications that compute the centrality of many groups in order to identify new properties. Our method is based on the concept of path betweenness centrality defined in this paper. We demonstrate how the method can be used to find the most prominent group. Then, we apply the method for epidemic control in communication networks. We also show how the method can be used to evaluate distributions of group betweenness centrality and its correlation with group degree. The method may assist in finding further properties of complex networks and may open a wide range of research opportunities.
TL;DR: This paper introduces a model framework to analyze the mediation of network structure by spatial embedding and model connectivity as dependent on the distance between network nodes, which is able to demonstrate, in a quite general setting, some constraints of spatial embeddedding on connectivity.
Abstract: Many real-world networks analyzed in modern network theory have a natural spatial element; e.g., the Internet, social networks, neural networks, etc. Yet, aside from a comparatively small number of somewhat specialized and domain-specific studies, the spatial element is mostly ignored and, in particular, its relation to network structure disregarded. In this paper we introduce a model framework to analyze the mediation of network structure by spatial embedding; specifically, we model connectivity as dependent on the distance between network nodes. Our spatially embedded random networks construction is not primarily intended as an accurate model of any specific class of real-world networks, but rather to gain intuition for the effects of spatial embedding on network structure; nevertheless we are able to demonstrate, in a quite general setting, some constraints of spatial embedding on connectivity such as the effects of spatial symmetry, conditions for scale free degree distributions and the existence of small-world spatial networks. We also derive some standard structural statistics for spatially embedded networks and illustrate the application of our model framework with concrete examples.
TL;DR: In this article, the authors present the application of network theory to the Dutch paym ent system with specific attention to systemic stability, and show that fast network development takes place in the early phase of network formation of about one hour and slower development afterwards.
Abstract: We present the application of network theory to the Dutch paym ent system with specific attention to systemic stability. The network nodes comprise of domestic bank s, large international banks and TARGET countries, the links are established by payments bet ween the nodes. Traditional measures (transactions, values) first show payments are relativel y well behaved through time and that the system does not contain a group of significant structural net receiver s or payers among the participant institutions. Structural circular flows do, however, exist in the system, most prominently a large circular net flow between TARGET countries. Analysis of the properties of prominent network measures over time shows that fast network development takes place in the early phase of network formation of about one hour and slower development afterwards. The payment network is small (in actual nodes and links), compact (in path length and eccentri city) and sparse (in connectivity) for all time periods. In the long run, a mere 12% of the possible nu mber of interbank connections is ever used and banks are on average only 2 steps apart. Relations in the ne twork tend to be reciprocal. Our results also indicate that the network is susceptible to directed attacks. In a final section we show that the recent ‘sub prime’ turmoil in credit markets has not mater ially affected the network structure.
TL;DR: This paper presents an individual-based analytical framework for modeling the spatial and temporal heterogeneity in the disease transmission that specifies a network model structure and associated parameters that allows the representation of discrete individuals, individualized interactions, and interaction patterns in a network of human contact.
Abstract: The spread of communicable diseases through a population is an intrinsic spatial and temporal process. This paper presents an individual-based analytical framework for modeling the spatial and temporal heterogeneity in the disease transmission. The framework specifies a network model structure and six associated parameters. These parameters describe the properties of nodes, the properties of links, and the topology of the network. Through this model structure and associated parameters, this framework allows the representation of discrete individuals, individualized interactions, and interaction patterns in a network of human contact. The explicit representation of the spatial distribution and mobility of individuals in particular facilitates the modeling of spatial heterogeneity in the disease transmission.
TL;DR: A method for motif-based centrality analysis is presented and two extensions are discussed which broaden the idea of motif- based centrality to specific functions of particular motif elements, and to the consideration of classes of related motifs.
TL;DR: In this article, the authors argue that cultural and religious change can be understood as emergent phenomena through analysis of the variability and dynamics of these interconnections, which is in conflict with evolutionary theory.
Abstract: Network theory recognizes that ideas and technology are transmitted along social interconnections, and cultural and religious change can be understood as emergent phenomena through analysis of the variability and dynamics of these interconnections. ‘Information cascade’ describes the diffusion of information across a network, providing, when combined with sociological theory of religious conversion, a way of re-approaching the success of the monotheistic ‘innovation’. Instead of viewing success as a measure of inherent merit, using networks means the observed outcomes need not be ‘superior’. This is in conflict with evolutionary theory, and this paper attempts to begin to reconcile these differing approaches.
TL;DR: In this paper, the functional centrality is introduced as a generalization of the subgraph centrality, and a general method for characterizing nodes in the graph according to the number of closed walks starting and ending at the node is proposed.
Abstract: In this article, we introduce the functional centrality as a generalization of the subgraph centrality. We propose a general method for characterizing nodes in the graph according to the number of closed walks starting and ending at the node. Closed walks are appropriately weighted according to the topological features that we need to measure.
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TL;DR: In this paper, the authors investigate how rural households form the links through which they provide and/or get economic support, and whether the connection structure of the community affects the formation of these links.
Abstract: In developing countries, whenever formal economic and financial institutions lack strength, households are forced to rely on risk sharing and other informal arrangements based on pre-existing interpersonal relationships. This paper takes a network perspective to investigate how rural households form the links through which they provide and/or get economic support, and whether the connection structure of the community affects the formation of these links. I test the hypothesis that indirect contacts matter, that is, agents take into account not only potential partners’ characteristics, but also their position with respect to all other agents. A network formation framework with fully heterogeneous agents is first presented, following Jackson and Wolinsky (1996), an estimation procedure is then proposed and applied to data on a village in rural Tanzania. Results show that when agents evaluate the net advantage of forming a link they also consider the relative position and the wealth of indirect partners. My paper contributes to both network theory and the literature on risk sharing arrangements in that it proposes an innovative procedure to estimate endogenous network formation models, and provides evidence that network structure has an explanatory value disregarded by all previous studies, which are focused on direct relations only.
22 Feb 2007
TL;DR: In this paper, the authors explore the role of social integration on altruistic behavior and find that social integration has a positive effect on giving: the larger social isolation within the group, the more likely it is the emergence of selfish behavior.
Abstract: This paper explores the role of social integration on altruistic behavior. To this aim, we develop a two-stage experimental protocol based on the classic Dictator Game. In the first stage, we ask a group of 77 undergraduate students in Economics to elicit their social network; in the second stage, each of them has to unilaterally decide over the division of a fixed amount of money to be shared with another anonymous member in the group. Our experimental design allows to control for other variables known to be relevant for altruistic behavior: framing and friendship/acquaintance relations. Consistently with previous research, we find that subjects favor their friends and that framing enhances altruistic behavior. Once we control for these effects, social integration (measured by betweenness, a standard centrality measure in network theory) has a positive effect on giving: the larger social isolation within the group, the more likely it is the emergence of selfish behavior. These results suggest that information on the network structure in which subjects are embedded is crucial to account for their behavior.
06 Aug 2007
TL;DR: A new model of dependence centrality is proposed, based on shortest paths between the pair of nodes, which discusses how intelligence investigation agencies could benefit from this measure and compares it with traditional data mining techniques.
Abstract: A new model of dependence centrality is proposed. The centrality measure is based on shortest paths between the pair of nodes. We apply this measure with the demonstration of a small network example. The comparisons are made with betweenness centrality. We discuss how intelligence investigation agencies could benefit from the proposed measure. In addition to that we argue about the investigative data mining techniques we are using, and a comparison is provided with traditional data mining techniques.
TL;DR: A game theory based framework, named games network, is presented for modeling biological interactions which is a signal transduction pathway involved in cancer cell migration and has enabled a better understanding of the regulation involved in the PAs system.
Abstract: In this article, we present a game theory based framework, named games network, for modeling biological interactions. After introducing the theory, we more precisely describe the methodology to model biological interactions. Then we apply it to the plasminogen activator system (PAs) which is a signal transduction pathway involved in cancer cell migration. The games network theory extends game theory by including the locality of interactions. Each game in a games network represents local interactions between biological agents. The PAs system is implicated in cytoskeleton modifications via regulation of actin and microtubules, which in turn favors cell migration. The games network model has enabled us a better understanding of the regulation involved in the PAs system.
01 Jan 2007
TL;DR: A model for analyzing the spread of epidemics in adisconnected mobile network is introduced, based on an extension, to a dynamic setting, of the eigenvector centrality principle introduced by two of the authors for the case of static net-works.
Abstract: Telenor R&ISnaroyveien 30N-1331 Fornebu (Norway)name.surname@telenor.comSummary. In this chapter we introduce a model for analyzing the spread of epidemics in adisconnected mobile network. The work is based on an extension, to a dynamic setting, ofthe eigenvector centrality principle introduced by two of the authors for the case of static net-works. The extension builds on a new definition of
TL;DR: This paper utilizes the Shapley value principle, which assigns a rank to players in a coalition based upon their individual contributions to the collective profit of that coalition, to assess the contributions of individual brain structures to the graph derived from the global connectivity network.
Abstract: Recent applications of network theory to brain networks as well as the expanding empirical databases of brain architecture spawn an interest in novel techniques for analyzing connectivity patterns in the brain. Treating individual brain structures as nodes in a directed graph model permits the application of graph theoretical concepts to the analysis of these structures within their large-scale connectivity networks. In this paper, we explore the application of concepts from graph and game theory toward this end. Specifically, we utilize the Shapley value principle, which assigns a rank to players in a coalition based upon their individual contributions to the collective profit of that coalition, to assess the contributions of individual brain structures to the graph derived from the global connectivity network. We report Shapley values for variations of a prefrontal network, as well as for a visual cortical network, which had both been extensively investigated previously. This analysis highlights particular nodes as strong or weak contributors to global connectivity. To understand the nature of their contribution, we compare the Shapley values obtained from these networks and appropriate controls to other previously described nodal measures of structural connectivity. We find a strong correlation between Shapley values and both betweenness centrality and connection density. Moreover, a stepwise multiple linear regression analysis indicates that approximately 79% of the variance in Shapley values obtained from random networks can be explained by betweenness centrality alone. Finally, we investigate the effects of local lesions on the Shapley ratings, showing that the present networks have an immense structural resistance to degradation. We discuss our results highlighting the use of such measures for characterizing the organization and functional role of brain networks.
01 Sep 2007
TL;DR: A proof of concept that an artificial lymphocyte (ALC) neighbourhood can cluster data in a dynamic environment is provided that LNNAIS only requires one pass through the training data of antigen patterns for clustering.
Abstract: The artificial immune system (AIS) is inspired by the functioning of the natural immune system. There are different theories with regards to the organisational behaviour of the natural immune system. One of these theories is the network theory. In this paper a novel network based AIS model is proposed. The proposed Local Network Neighbourhood Artificial Immune System (LNNAIS) is inspired by the network topology of lymphocytes to learn the antigen structure from one another. LNNAIS has a different interpretation of the network theory compared to existing network based AIS models. LNNAIS uses a concept of an artificial lymphocyte (ALC) neighbourhood to determine the network links between the ALCs. The purpose of this paper is to provide a proof of concept that an artificial lymphocyte (ALC) neighbourhood can cluster data in a dynamic environment. LNNAIS only requires one pass through the training data of antigen patterns for clustering.
14 May 2007
TL;DR: It is shown that both node degree and node centrality are not necessarily evidence of its significance, and some medium degree nodes with medium centrality measure prove to be crucial for efficient routing in the Internet AS graph.
Abstract: In networks characterized by broad degree distribution, such as the Internet AS graph, node significance is often associated with its degree or with centrality metrics which relate to its reachability and shortest paths passing through it. Such measures do not consider availability of efficient backup of the node and thus often fail to capture its contribution to the functionality and resilience of the network operation. In this paper we suggest the Quality of Backup (QoB) and Alternative Path Centrality (APC) measures as complementary methods which enable analysis of node significance in a manner which considers backup. We examine the theoretical significance of these measures and use them to classify nodes in the Internet AS graph while applying the BGP valley-free routing restrictions. We show that both node degree and node centrality are not necessarily evidence of its significance. In particular, some medium degree nodes with medium centrality measure prove to be crucial for efficient routing in the Internet AS graph.
TL;DR: In this article, the authors investigate the influence of heterogeneity and self-organization on the transport properties of granular matter, with particular attention to heat conduction, and find that selforganization in the granular network promotes efficient transport.
Abstract: Granular matter may be one of the simplest prototypes of what have come to be regarded as complex systems —systems where simple interactions can lead to rich, often surprising, global behavior. For example, interparticle contacts in a granular system give rise to networks that are 1) heterogeneous, i.e., a few particles support high compressive force, while many others support relatively little, and 2) self-organized, i.e., spatially correlated strong forces tend to form a sub-network of interconnecting "force chains". Using numerical simulations, we investigate the influence of heterogeneity and self-organization on the transport properties of granular matter, with particular attention to heat conduction —a phenomenon of ubiquitous importance in engineering and nature. We find that self-organization in the granular network promotes efficient transport. Furthermore, a network-attack experiment suggests that contacts with high betweenness centrality, not necessarily those with highest local heat transfer coefficient, most significantly influence transport behavior. We find that concepts of network theory yield valuable insight —both qualitative and quantitative— into the observed behavior.
TL;DR: In this article, the authors make extensive use of the Java Universal Network/Graph Framework (JUNG) via J/Link technology and facilitate communication of Mathematica graph structures to other network analysis programs such as Pajek by developing methods of import and export using GraphML.
Abstract: In common law jurisdictions such as the United States, courts frequently resolve disputes by citation and analysis of prior legal cases. The law may thus be thought of as a giant network containing textual information embedded in cases (nodes) and relationship information called citations (arcs) going from node to node. In recent years, the science of studying networks has developed [1] but, while there have been some primitive attempts to look at subsets of the vast legal network, until recently there has been little done to take advantage of modern technology and network theory. This article borrows techniques developed largely in sociology [2, 3] and physics and uses modern technology to learn about the law simply by studying its network structure. The article makes extensive use of the Java Universal Network/Graph Framework (JUNG) [4] via J/Link technology and facilitates communication of Mathematica graph structures to other network analysis programs such as Pajek by developing methods of import and export using GraphML. Although this article focuses on tool building, it is my hope that these efforts, along with pending publications on legal networks by Professor Thomas A. Smith of the University of San Diego Law School [5] and Professors James H. Fowler and Sangick Jeon of the University of California, Davis [6], will catalyze a set of studies in this field that will expand to cover other judicial systems and yet more sophisticated analysis of network information.
TL;DR: The issue of whether contemporary ‘social network theory’ provides a valid working model for understanding theoric networks (or the theoric network) is explored, looking at such issues as connectivity, clustering, and the Strogatz–Watts principle.
Abstract: The term ‘theoric network’ can be understood either as a network made up of all the cities that send festival delegates (theoroi) to one particular festival, together with the city that organizes the festival itself, or as the overall ‘hyper-network’, comprising the sum of all such theoric networks. The paper explores the issue of whether contemporary ‘social network theory’ provides a valid working model for understanding theoric networks (or the theoric network), looking at such issues as connectivity, clustering, and the Strogatz–Watts principle.
04 Jun 2007
TL;DR: A new complex measure of the degree centrality is introduced including weighted ties possible for use of the analysis of co-authorship or citation networks, which shows the whole network on the Web has a more centralized structure than the bibliographic network.
Abstract: There is a rapid increase of network analysis in several scientific disciplines beginning some decades ago. In the literature there are few studies on networks with weighted ties since they not only need more complex formulas but need a process of quantification when quantitative empirical data are not directly available. However quantitative empirical data are directly available under the condition of using bibliometric or webometric data. In conclusion a new Complex Measure of the Degree Centrality is introduced including weighted ties possible for use of the analysis of co-authorship or citation networks. Both co-authorship relations and citations are well quantified data (weighted ties). This new measure is applied to a bibliographic co-authorship network and its reflection on the Web as an example. The new measures of degree centrality show the whole network on the Web has a more centralized structure than the bibliographic network.