Showing papers on "Network theory published in 2009"

Network Analysis for International Relations

Abstract: International relations research has regarded networks as a particular mode of organization, distinguished from markets or state hierarchies. In contrast, network analysis permits the investigation and measurement of network structures—emergent properties of persistent patterns of relations among agents that can define, enable, and constrain those agents. Network analysis offers both a toolkit for identifying and measuring the structural properties of networks and a set of theories, typically drawn from contexts outside international relations, that relate structures to outcomes. Network analysis challenges conventional views of power in international relations by defining network power in three different ways: access, brokerage, and exit options. Two issues are particularly important to international relations: the ability of actors to increase their power by enhancing and exploiting their network positions, and the fungibility of network power. The value of network analysis in international relations has been demonstrated in precise description of international networks, investigation of network effects on key international outcomes, testing of existing network theory in the context of international relations, and development of new sources of data. Partial or faulty incorporation of network analysis, however, risks trivial conclusions, unproven assertions, and measures without meaning. A three-part agenda is proposed for future application of network analysis to international relations: import the toolkit to deepen research on international networks; test existing network theories in the domain of international relations; and test international relations theories using the tools of network analysis.

Graph models of habitat mosaics

Abstract: Graph theory is a body of mathematics dealing with problems of connectivity, flow, and routing in networks ranging from social groups to computer networks. Recently, network applications have erupted in many fields, and graph models are now being applied in landscape ecology and conservation biology, particularly for applications couched in metapopulation theory. In these applications, graph nodes represent habitat patches or local populations and links indicate functional connections among populations (i.e. via dispersal). Graphs are models of more complicated real systems, and so it is appropriate to review these applications from the perspective of modelling in general. Here we review recent applications of network theory to habitat patches in landscape mosaics. We consider (1) the conceptual model underlying these applications; (2) formalization and implementation of the graph model; (3) model parameterization; (4) model testing, insights, and predictions available through graph analyses; and (5) potential implications for conservation biology and related applications. In general, and for a variety of ecological systems, we find the graph model a remarkably robust framework for applications concerned with habitat connectivity. We close with suggestions for further work on the parameterization and validation of graph models, and point to some promising analytic insights.

Applying centrality measures to impact analysis: A coauthorship network analysis

Abstract: Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988–2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis. © 2009 Wiley Periodicals, Inc.

Shaping Sustainable Value Chains: Network Determinants of Supply Chain Governance Models

Abstract: Although the characteristics and advantages of interorganizational governance models based on extensive collaboration are well established in the literature, inquiry has only recently extended to sustainable supply chain management, highlighting the potential benefits of combining the integration of social and environmental issues concerning the supply chain with governance models based on joint decision making and extensive cooperation. Yet, firms still differ in both the pervasiveness of such collaborative approaches along the value chain and the extent to which sustainability issues are addressed to the advantage of all parties involved. In an attempt to predict variety in the governance models related to sustainability along the value chain, we propose a theoretical model that identifies and frames four sustain- able supply chain governance (SSCG) models, resulting from combinations of supply chain network density and centrality of the focal organizations. We show how, as centrality increases, firms are able to exert influence over their network, coordinating integrated approaches along the value chain. Moreover, as high centrality combines with increasing interconnectedness of the actors within a supply chain network, instrumental approaches are progressively replaced by more relational attitudes aimed at joint value creation among partners. Conditions for SSCG models’ success and the main benefits gained by firms in different structural contexts are also discussed.

Bigger and safer: the diffusion of competitive advantage

Abstract: Research on the diffusion of technologies that give competitive advantage is needed to understand the role of technology in competition. Predictions on which firms first obtain useful technologies are made by cluster theory, which holds that the diffusion is geographically bounded, and network theory, which holds that adoption is more rapid in central network positions. These predictions can be evaluated using data on the diffusion of supplier innovations that give competitive advantage to firms in the buyer industry. Here, the diffusion of new ship types is studied using the heterogeneous diffusion model and data on shipping firm-shipbuilder networks, showing that valuable innovations remain rare because they are not adopted by distant firms in geographical and network space. The strong influence of geographically dispersed interfirm networks on technology diffusion justifies a greater role of interorganizational networks in the theory of competitive advantage. Copyright © 2008 John Wiley & Sons, Ltd.

Social Network Analysis

Abstract: Power no longer resides exclusively (if at all) in states, institutions, or large corporations. It is located in the networks that structure society. Social network analysis seeks to understand networks and their participants and has two main focuses: the actors and the relationships between them in a specific social context.

The 'wired' universe of organic chemistry

Abstract: The millions of reactions performed and compounds synthesized by organic chemists over the past two centuries connect to form a network larger than the metabolic networks of higher organisms and rivalling the complexity of the World Wide Web. Despite its apparent randomness, the network of chemistry has a well-defined, modular architecture. The network evolves in time according to trends that have not changed since the inception of the discipline, and thus project into chemistry's future. Analysis of organic chemistry using the tools of network theory enables the identification of most 'central' organic molecules, and for the prediction of which and how many molecules will be made in the future. Statistical analyses based on network connectivity are useful in optimizing parallel syntheses, in estimating chemical reactivity, and more.

Networks as Culturally Constituted Processes A Comparison of Relational Sociology and Actor-network Theory

Abstract: This article connects two current, typically separate strands in network thinking that treat ‘culture’ and ‘structure’ as intermingled rather than as autonomous entities of a duality. It reviews and compares two different traditions, the ‘cultural turn’ in social network analysis and actor-network theory, which both view networks as culturally constituted processes. The article argues that the two approaches share many conceptual similarities, although important differences remain. They differ on what kinds of actors ascribe meaning to others. Furthermore, the article argues that some conceptual similarities have turned into methodological points of convergence in data analysis. The article suggests economic sociology as one possible area of research where the two approaches productively connect.

Robustness of centrality measures under uncertainty: Examining the role of network topology

Abstract: This study investigates the topological form of a network and its impact on the uncertainty entrenched in descriptive measures computed from observed social network data, given ubiquitous data-error. We investigate what influence a network's topology, in conjunction with the type and amount of error, has on the ability of a measure, derived from observed data, to correctly approximate the same of the ground-truth network. By way of a controlled experiment, we reveal the differing effect that observation error has on measures of centrality and local clustering across several network topologies: uniform random, small-world, core-periphery, scale-free, and cellular. Beyond what is already known about the impact of data uncertainty, we found that the topology of a social network is, indeed, germane to the accuracy of these measures. In particular, our experiments show that the accuracy of identifying the prestigious, or key, actors in a network--according observed data--is considerably predisposed by the topology of the ground-truth network.

User position measures in social networks

Abstract: Network analysis offers many centrality measures that are successfully utilized in the process of investigating the social network profile. The most important and representative measures are presented in the paper. It includes indegree centrality, proximity prestige, rank prestige, node position, outdegree centrality, eccentrality, closeness centrality, and betweenes centrality. Both feature analysis and experimental comparative studies revealed the general profile of selected measures.

A measure of authors’ centrality in co-authorship networks based on the distribution of collaborative relationships

Abstract: Although there are many measures of centrality of individuals in social networks, and those centrality measures can be applied to the analysis of authors’ importance in co-authorship networks, the distribution of an author’s collaborative relationships among different communities has not been considered. This distribution or extensity is an important aspect of authors’ activity. In the present study, we will propose a new measure termed extensity centrality, taking into account the distribution of an author’s collaborative relationships. In computing the strength of collaborative ties, which is closely related to the extensity centrality, we choose Salton’s measure. We choose the ACM SIGKDD data as our testing data set, and analyze the result of authors’ importance from different points of view.

Time evolution of epidemic disease on finite and infinite networks.

Abstract: Mathematical models of infectious diseases, which are in principle analytically tractable, use two general approaches. The first approach, generally known as compartmental modeling, addresses the time evolution of disease propagation at the expense of simplifying the pattern of transmission. The second approach uses network theory to incorporate detailed information pertaining to the underlying contact structure among individuals while disregarding the progression of time during outbreaks. So far, the only alternative that enables the integration of both aspects of disease propagation simultaneously while preserving the variety of outcomes has been to abandon the analytical approach and rely on computer simulations. We offer an analytical framework, that incorporates both the complexity of contact network structure and the time progression of disease spread. Furthermore, we demonstrate that this framework is equally effective on finite- and "infinite"-size networks. This formalism can be equally applied to similar percolation phenomena on networks in other areas of science and technology.

Can Betweenness Centrality Explain Traffic Flow

Abstract: Traffic flow is the process of physical agents moving along an urban travel network. These agents are autonomous, purposeful, flexible, and volatile. They establish a social network: agents near to each other can communicate and interact (other social ties, like kinship or friendship, are not considered here). Since the agents are mobile this social network is highly dynamic. Also agents are volatile. They enter traffic at any time, and leave as soon as they have reached their destination. The places where they emerge or disappear are distributed over space and time, but not in a random manner. Additionally, agents in urban traffic are purposeful. They have individual travel, sensing and communication capabilities, maybe even preferences, and a specific travel demand (to reach a destination by a specified time or specified costs). Especially, during travel they can interact with their fellow agents, be it by coordination (communication) or collaboration (transport), and they can sense and act in their physical environment, and thus, change their travel plans at any time to satisfy their travel demand. This means travel plans—if not the underlying travel demand itself—can be dynamic. This social network of agents in traffic can also be characterized by centrality measures; however, these measures are attributes of the agents, not of the nodes of the physical travel network, and they are constantly changing—hence, infeasible to track in a central database. With all these behavioral observations of urban traffic at hand, we are interested in whether, and if, to what extent the physical structure of the network, characterized by betweenness centrality, determines the traffic generated by the agents. Betweenness centrality is based on shortest paths in the network, and the typical (implicit) assumption of rational agents suggests expecting that they travel shortest paths. Accordingly one can expect that nodes on many shortest paths in the urban travel network attract much traffic, and by this way, betweenness centrality can characterize the patterns of traffic flow or traffic density. This view is supported by evidence reported in the literature (see, e.g., Hillier et al. 1993; Penn et al. 1998; Jiang 2008b). But two factors are shaking our confidence in this argument. First, we know that human agents act at most bounded rational (Gigerenzer 2008), and hence, their chosen distance function in determining shortest paths is likely not to be topological (alone). Second, the depicted dynamics of travel behavior cannot be found out of the characteristics of a static network. Therefore, whether betweenness centrality can explain traffic flow is a valid question. We will try to prove the hypothesis that betweenness centrality of the physical travel network is insufficient to explain traffic flow.

Death of Distance in Science? A Gravity Approach to Research Collaboration

Abstract: One of the major transitions in recent scientific research is the rise of network theory motivating a variety∈dexvariety of new research programmes in and across various disciplines. Economic geography∈dexgeography has been no exception. The work on networks in economic geography can be divided into two types of research. First, there are studies on inter-firm networks and their impact on firm performance. For a large part, such studies have been carried out in the context of geographical clusters, which are often characterised by strong network relations (Uzzi, 1997). A second approach, an example of which is presented below, concerns the study of inter-regional networks and their impact on regional growth. Here, the unit of analysis are territories, typically sub-national regions. The interest in this topic stems from Castells (1996) and others who have argued that regional growth increasingly depends on a region’s position in global networks rather than its specific local characteristics such as institutions, endowments and amenities (‘space of flows’ versus the ‘space of places’).

The social aspects of industrial symbiosis: the application of social network analysis to industrial symbiosis networks

Abstract: This paper explores the potential of the application of social network analysis and network theory to the field of Industrial Symbiosis (IS), both as a methodological stance and as a conceptual framework, as a way to approach an understanding of the complexity of IS networks. We argue that the network approach can provide a comprehensive framework for the understanding of the social aspects behind material and energy exchanges. Aspects such as the structure of networks and the exchange and social conditions under which networks are likely to emerge and thrive are examined in the light of this approach. The main concepts of social network analysis and network theory are introduced and its applicability to IS discussed. A methodology is proposed to be applied to research the social and institutional aspects of IS. Some conclusions from the application of the methodology, its potential and shortcomings are presented.

Networks, resources, political strategy and climate policy

Abstract: This analysis is designed to show how policy network theory can be used to gain insights into the politics of climate change and climate policy. A version of policy network theory is set out based on the idea that policy networks are created and sustained by interdependencies among political actors. This theory identifies the main types of resources that are exchanged, and the main kinds of political actors that are likely to engage in resource exchange in the field of climate policy. Policy network theory is then used to unpack the main strategic options that are available to governments. The analysis concludes by listing 10 specific implications for governments that want to take more effective action against climate change while avoiding significant political damage.

Fast centrality approximation in modular networks

Abstract: Measuring the centrality of nodes in real-world networks has remained an important task in the technological, social, and biological network paradigms carrying implications on their analysis and applications. Exact inference of centrality values is infeasible in large networks due to the need to solve the all-pairs shortest path problem. We introduce a framework to approximate node centralities in real-world networks that are known to exhibit modularity, i.e., the presence of dense subgraphs or communities, which are themselves sparsely connected. We also propose a novel centrality measure known as Community Inbetweenness that ranks nodes based solely on community information. In a modular network of size n with √n(1/2) evenly sized communities and m edges, our framework requires linear time O(m) and O(√nm) time for the approximation of closeness and betweenness respectively. Utilizing a recently proposed linear time method in community detection, our approximation techniques are faster than traditional sampling algorithms, applicable in real-time distributed environments, and offer highly comparable results.

Exploring the Synergy Between Industrial Ecology and System of Systems to Understand Complexity A Case Study in Air Transportation

Abstract: Summary Twoobjectivesarepursuedinthisarticle.First,fromamethodological perspective, we explore the relationships among the constructs of complex adaptive systems, systems of systems, and industrial ecology. Through examination of central traits of each, we find that industrial ecology and system of systems present complementary frameworks for posing systemic problems in the context of sociotechnical applications. Furthermore, we contend that complexity science (the basis for the study of complex adaptive systems) provides a natural and necessary foundation and set of tools to analyze mechanisms such as evolution, emergence, and regulation in these applications. The second objective of the article is to illustrate the use of two tools from complexity sciences to address a network transition problem in air transportation framed from the system-of-systems viewpoint and shaped by an industrial ecology perspective. A stochastic simulation consisting of network theory analysis combined with agent-based modeling to study the evolution of an air transport network is presented. Patterns in agent behavior that lead to preferred outcomes across two scenarios are observed, and the implications of these results for decision makers are described. Furthermore, we highlight the necessity for future efforts to combine the merits of both system of systems and industrial ecology in tackling the issues of complexity in such large-scale, sociotechnical problems.

Impact of hierarchical modular structure on ranking of individual nodes in directed networks

Abstract: Many systems, ranging from biological and engineering systems to social systems, can be modeled as directed networks, with links representing directed interaction between two nodes. To assess the importance of a node in a directed network, various centrality measures based on different criteria have been proposed. However, calculating the centrality of a node is often difficult because of the overwhelming size of the network or because the information held about the network is incomplete. Thus, developing an approximation method for estimating centrality measures is needed. In this study, we focus on modular networks; many real-world networks are composed of modules, where connection is dense within a module and sparse across different modules. We show that ranking-type centrality measures, including the PageRank, can be efficiently estimated once the modular structure of a network is extracted. We develop an analytical method to evaluate the centrality of nodes by combining the local property (i.e. indegree and outdegree of nodes) and the global property (i.e. centrality of modules). The proposed method is corroborated by real data. Our results provide a linkage between the ranking-type centrality values of modules and those of individual nodes. They also reveal the hierarchical structure of

Inverse problems in queueing theory and Internet probing

Abstract: Queueing theory is typically concerned with the solution of direct problems, where the trajectory of the queueing system, and laws thereof, are derived based on a complete specification of the system, its inputs and initial conditions. In this paper we point out the importance of inverse problems in queueing theory, which aim to deduce unknown parameters of the system based on partially observed trajectories. We focus on the class of problems stemming from probing based methods for packet switched telecommunications networks, which have become a central tool in the measurement of the structure and performance of the Internet. We provide a general definition of the inverse problems in this class and map out the key variants: the analytical methods, the statistical methods and the design of experiments. We also contribute to the theory in each of these subdomains. Accordingly, a particular inverse problem based on product-form queueing network theory is tackled in detail, and a number of other examples are given. We also show how this inverse problem viewpoint translates to the design of concrete Internet probing applications.

Centralities: capturing the fuzzy notion of importance in social graphs

Abstract: The increase of interest in the analysis of contemporary social networks, for both academic and economic reasons, has highlighted the inherent difficulties in handling large and complex structures. Among the tools provided by researchers for network analysis, the centrality notion, capturing the importance of individuals in a graph, is of particular interest. Despite many definitions and implementations of centrality, no clear advantage is given to a particular paradigm for the study of social network characteristics. In this paper we review, compare and highlight the strengths of different definitions of centralities in contemporary social networks.

A foundation for developing a methodology for social network sampling

Abstract: Researchers are increasingly turning to network theory to understand the social nature of animal populations. We present a computational framework that is the first step in a series of works that will allow us to develop a quantitative methodology of social network sampling to aid ecologists in their social network data collection. To develop our methodology, we need to be able to generate networks from which to sample. Ideally, we need to perform a systematic study of sampling protocols on different known network structures, as network structure might affect the robustness of any particular sampling methodology. Thus, we present a computational tool for generating network structures that have user-defined distributions for network properties and for key measures of interest to ecologists. The user defines the values of these measures and the tool will generate appropriate network randomizations with those properties. This tool will be used as a framework for developing a sampling methodology, although we do not present a full methodology here. We describe the method used by the tool, demonstrate its effectiveness, and discuss how the tool can now be utilized. We provide a proof-of-concept example (using the assortativity measure) of how such networks can be used, along with a simulated egocentric sampling regime, to test the level of equivalence of the sampled network to the actual network.

A Dynamic Model of Network Formation with Strategic Interactions

Abstract: In order to understand the different characteristics observed in real-world networks, one needs to analyze how and why networks form, the impact of network structure on agents' outcomes, and the evolution of networks over time. For this purpose, we combine a network game introduced by Ballester et al. (2006), where the Nash equilibrium action of each agent is proportional to her Bonacich centrality, with an endogenous network formation process. Links are formed on the basis of agents' centrality while the network is exposed to a volatile environment introducing interruptions in the connections between agents. A remarkable feature of our dynamic network formation process is that, at each period of time, the network is a nested split graph. This graph has very nice mathematical properties and are relatively easy to characterize. We show that there exists a unique stationary network (which is a nested split graph) whose topological properties completely match features exhibited by real-world networks. We also find that there exists a sharp transition in efficiency and network density from highly centralized to decentralized networks.

Axiomatic Foundations of Centrality in Networks

Abstract: The social and economic networks literature commonly uses different measures to capture various notions of centrality. Given the structural features of a network, there is limited consensus on how to select the appropriate centrality measure. This paper provides an axiomatic characterization of Degree, Closeness and Decay centrality measures and argues that such axiomatizations are the correct way to distinguish between centrality measures. Further, I show that these centrality measures belong to the same family of measures. This result shows why they have been found to be correlated in empirical work.

Law as a Seamless Web? Comparison of Various Network Representations of the United States Supreme Court Corpus (1791-2005)

Abstract: Citation networks are a cornerstone of network research and have been important to the general development of network theory. Citation data have the advantage of constituting a well-defined set where the nature of nodes and edges is reasonably well specified. Much interesting and important work has been done in this vein, with respect to not only academic but also judicial citation networks. For example, previous scholarship focuses upon broad citation patterns, the evolution of precedent, and time-varying change in the likelihood that communities of cases will be cited. As research of judicial citation and semantic networks transitions from a strict focus on the structural characteristics of these networks to the evolutionary dynamics behind their growth, it becomes even more important to develop theoretically coherent and empirically grounded ideas about the nature of edges and nodes. In this paper, we move in this direction on several fronts. We compare several network representations of the corpus of United States Supreme Court decisions (1791-2005). This corpus is not only of seminal importance, but also represents a highly structured and largely self-contained body of case law. As constructed herein, nodes represent whole cases or individual 'opinion units' within cases. Edges represent either citations or semantic connections. As our broader goal is to better understand American common law development, we are particularly interested in the union, intersect and compliment of these various citation networks as they offer potential insight into the long-standing question of whether 'law is a seamless web'? We believe the characterization of law’s interconnectedness is an empirical question well suited to the tools of computer science and applied graph theory. While much work still remains, the analysis provided herein is designed to advance the broader cause.

Kinship Network Analysis

Abstract: Kinship, like language, is a structure, not a substance. The distinctive features of kinship networks reside less in how their constitutive ties - be they biological, jural, ritual, symbolic, or whatever - are defined and established than in the way these ties are organized. Kinship network theory is thus not just another "application" of general network theoretic methods to a particular social domain but a specific branch of social network theory in itself, defined by its own axioms and described by its own theorems. This article sets out to present these axioms and theorems.