Showing papers on "Network theory published in 2022"

A straightforward edge centrality concept derived from generalizing degree and strength

Abstract: Vertex degree-the number of edges that are incident to a vertex-is a fundamental concept in network theory. It is the historically first and conceptually simplest centrality concept to rate the importance of a vertex for a network's structure and dynamics. Unlike many other centrality concepts, for which joint metrics have been proposed for both vertices and edges, by now there is no concept for an edge centrality analogous to vertex degree. Here, we propose such a concept-termed nearest-neighbor edge centrality-and demonstrate its suitability for a non-redundant identification of central edges in paradigmatic network models as well as in real-world networks from various scientific domains.

Problemistic Search of the Embedded Firm: The Joint Effects of Performance Feedback and Network Positions on Venture Capital Firms’ Risk Taking

Abstract: The Behavioral Theory of the Firm suggests that performance below aspirations triggers problemistic search that can lead to risk taking. This prediction has received empirical support from most studies on the topic. However, this literature has typically focused on the internal determinants of firm search and risk-taking behavior and given little attention to the influences of social networks in which firms are embedded. To this end, we incorporate the network embeddedness perspective regarding firms’ network positions and their roles in firm decision making. We suggest that a firm’s search behavior is jointly directed by its performance feedback and network positions. Specifically, network brokerage and centrality play important yet distinct roles in guiding firm search behavior by differentially shaping the direction of problemistic search: high brokerage directs problemistic search to high-risk solutions, whereas high centrality directs problemistic search to low-risk solutions. Our theoretical predictions receive general empirical support based on analyses using longitudinal data from the Chinese venture capital industry. Our approach incorporates the crucial role of network structures into the problemistic search model and works toward building a problemistic search theory of the embedded firm.

Assessing Supply Chain Resilience During the Pandemic Using Network Analysis

Abstract: Disruptions induced by the COVID-19 pandemic have wreaked havoc in supply chain networks. To gain an understanding of the dynamics that had been at play, we construct a real supply chain network (scale-free) based on a seed firm (Apple), its customers, and its first- and second-tier suppliers, yielding a network of a total of 883 firms. We then use visualization to derive insight into various network characteristics and develop an agent-based model to capture the disruption of the network over a period of 400 days from the onset of the pandemic. The disruptions experienced by firms depend on the stringency of measures taken to curb the pandemic in their respective countries and the severity of disruptions experienced by suppliers in a specific region. We specifically find that spatial complexity, degree centrality, betweenness centrality, and closeness centrality have changed significantly throughout our observation period. We thus subsequently theorize on the influence of some of these characteristics on supply chain resilience (SCRes), and through our empirical tests, we find that, at the network level, Average degree and spatial complexity significantly influence SCRes. At the firm-level, we find that powerful firms within the network influence SCRes based on their betweenness centrality and closeness Centrality. Implications for managerial practice and academic research are discussed.

Unfolding research themes for industrial symbiosis and underlying theories

Abstract: This paper aims to analyze the research themes that are addressed in studies on industrial symbiosis (IS), from the perspective of theories. The paper method is a longitudinal analysis of retrospective nature from 1998 to 2021. This approach allowed to detects the main drivers and associated themes that structured researches on IS in the period. In general, Industrial Ecology Theory, Network Theory, Systems Theory and Organizational Theory were present in a considerable part of research themes on IS. It was observed that the IS research themes moved over the period analyzed to nine areas of study: (1) Waste as a resource, (2) development industrial symbiosis network, (3) circular economy and industrial symbiosis, (4) industrial co-localization, (5) energy innovations, (6) closed loop chains, (7) IS's social aspects, (8) regional-level metabolism, and (9) waste knowledge. In summary, the contributions of this paper are the thematic proposition of IS from the perspective of theories, pointing out the key-aspects; and presentation of a research themes' diagram towards the IS buildup.

Coherence Analysis of Symmetric Star Topology Networks

Abstract: The dynamics of complex networks are closely related to the topological structure. As an important research branch, the problem of network consensus has attracted more attention. In this paper, the first-order coherence of three kinds of symmetric star topology networks are studied by using the theory of network science. Firstly, three kinds of symmetric star topology network models are given. Secondly, the first-order coherence of these networks are calculated by using matrix theory. The relationships among the first-order coherence of the network and branch length and the number of branches change are obtained by numerical simulation. Finally, we found that the third network has the best consensus, and the change of branch length has more effective impact on network consensus.

Exploration of Network Theory to Evaluate Organizational Resilience

Abstract: Recent disasters and rapid changes in the environment have shown various open issues in organizational resilience, in particular the use of communications. We explore the use of Network Theory to study the robustness of communications in organizations regardless their structure and the communication mechanisms used. We focus on how a collapse in the communication mechanisms affects the communication structure in an organization. We use a case study based on an emergency management plan from a Nuclear Power Plant in Spain. We show that Network Theory along with the number of connected components in a network provides a cost-effective way to analyze the communication infrastructure and organizational relations. Network Theory also allows us to build awareness about the communication and information structure inside an organization, and to design a more robust communication network.

Analysis of turkey’s airport network structure and centrality in the opening-out period after the first wave of COVID-19: A complex network approach

Abstract: There are a few studies present analyzing air transport structures of the countries, regions, and the world by using complex network theory. Although Turkey has a complex air network with 56 airports, thorough research has not been carried out applying complex network analysis to reveal the structure of the airport network in Turkey yet. Furthermore, the fact that Turkey is a developing economy and the role of air transport in this development is undeniable, and its use as a transit area due to its geopolitical location, together with social and political factors, makes it important to understand this air transport network in detail. Consequently, in this paper, Turkey’s Airport Network (TAN) topology was explored by implementing concepts of complex network theory using domestic and international passenger flight data gathered from FlightRadar during the opening period after the first wave of Covid-19. The average path length and clustering coefficient for connectivity performance and centrality metrics (degree, betweenness, and closeness) were computed and the network correlations were also measured and compared with simulated random, small-world (SW), and scale-free (SF) network models. Also, structural similarities and differences with the air networks of other countries are revealed.

Continuous-time quantum walk based centrality testing on weighted graphs

Abstract: Centrality measure is an essential tool in network analysis and widely used in the domain of computer science, biology and sociology. Taking advantage of the speedup offered by quantum computation, various quantum centrality measures have been proposed. However, few work of quantum centrality involves weighted graphs, while the weight of edges should be considered in certain real-world networks. In this work, we extend the centrality measure based on continuous-time quantum walk to weighted graphs. We testify the feasibility and reliability of this quantum centrality using an ensemble of 41,675 graphs with various topologies and comparing with the eigenvector centrality measure. The average Vigna's correlation index of all the tested graphs with all edge weights in [1, 10] is as high as 0.967, indicating the pretty good consistency of rankings by the continuous-time quantum walk centrality and the eigenvector centrality. The intuitive consistency of the top-ranked vertices given by this quantum centrality measure and classical centrality measures is also demonstrated on large-scale weighted graphs. Moreover, the range of the continuous-time quantum walk centrality values is much bigger than that of classical centralities, which exhibits better distinguishing ability to pick the important vertices from the ones with less importance. All these results show that the centrality measure based on continuous-time quantum walk still works well on weighted graphs.

Network-based approaches for the investigation of microbial community structure and function using metagenomics-based data.

Abstract: Network-based approaches offer a powerful framework to evaluate microbial community organization and function as it relates to a variety of environmental processes. Emerging studies are exploring network theory as a method for data integration that is likely to be critical for the integration of 'omics' data using systems biology approaches. Intricacies of network theory and methodological and computational complexities in network construction, however, impede the use of these tools for translational science. We provide a perspective on the methods of network construction, interpretation and emerging uses for these techniques in understanding host-microbiota interactions.

Shapeshifting to address complexity: Advancing a typology of network evolution and transformation

Abstract: In practice, health and social services are delivered through purpose-oriented networks (PONs) that are often favored by government and philanthropic investment as an effective means for collectively solving complex social problems. Current theories examine the evolution of these groups by resting on the traditional organizational forms of market, hierarchy, and network, without a consideration of trajectories that show movement between organizational forms over time. This article utilizes the network itself as the unit of analysis within a larger network domain to examine the most common trajectories and changes in organizational forms over time. To date, little theory has been developed or applied to account for both endogenous characteristics and exogenous system-wide dynamics and their longitudinal effects on networks. As is appropriate in the early stages of developing new theories, this article addresses the foundational steps of first clarifying the phenomenon of interest with the creation of a typology of the ways in which networks evolve as organizational forms and suggesting future network-level and network domain research agendas.

Gromov centrality: A multiscale measure of network centrality using triangle inequality excess

Abstract: Centrality measures quantify the importance of a node in a network based on different geometric or diffusive properties, and focus on different scales. Here, we adopt a geometrical viewpoint to define a multiscale centrality in networks. Given a metric distance between the nodes, we measure the centrality of a node by its tendency to be close to geodesics between nodes in its neighborhood, via the concept of triangle inequality excess. Depending on the size of the neighborhood, the resulting Gromov centrality defines the importance of a node at different scales in the graph, and it recovers as limits well-known concepts such as the clustering coefficient and closeness centrality. We argue that Gromov centrality is affected by the geometric and boundary constraints of the network, and illustrate how it can help distinguish different types of nodes in random geometric graphs and empirical transportation networks.

A social network perspective on workplace inclusion: The role of network closure, network centrality, and need for affiliation

Abstract: Organizations are increasingly recognizing the important role employee inclusion perceptions play in promoting positive employee attitudes and behaviors. Although social networks are frequently cited as being a driver of perceived inclusion, little empirical work has examined the social network conditions that give rise to it. We address this gap by examining how both network position (indegree centrality) and network structure (network closure) relate to perceived workplace inclusion. We test our hypotheses with a sample of 364 professionals in a multinational pharmaceutical firm. We find that both indegree centrality and network closure are positively related to perceived workplace inclusion. The relationship between network centrality and perceived workplace inclusion is strengthened by a high level of network closure. In addition, the relationship between network closure and perceived workplace inclusion is strengthened by a high level of need for affiliation. Our results, therefore, suggest that both network centrality and closure play an important role in employee perceptions of inclusion and demonstrate the importance of considering need for affiliation as a boundary condition. We conclude by discussing the implications of these findings for theory and practice.

A new place for transport in urban network theory: The urban logistic network

Abstract: Transport history has developed in close association with urban network theory. However, this association has often remained implicit and not conceptualised. This article starts from an overview of the historiography on urban networks to question the limitations of historical urban network theory by highlighting the connection between an incomplete mapping of hinterlands and the prevalence of a neo-Christallerian model in the interpretation of their network shape. The concept of the “urban logistic network” is proposed as an alternative historical approach that focuses on the interaction between urban systems on the one hand, and transport and mobility on the other hand. In particular, it enables to clarify the conflated concepts of gateways and hinterlands and constructs a taxonomy that allows the examination of network patterns on a variety of geographical scales. It also identifies the variety of network shapes that are created in urban systems by different logistic connections.

Centrality measures in interval-weighted networks

Abstract: Abstract Centrality measures are used in network science to assess the centrality of vertices or the position they occupy in a network. There are a large number of centrality measures according to some criterion. However, the generalizations of the most well-known centrality measures for weighted networks, degree centrality, closeness centrality and betweenness centrality have solely assumed the edge weights to be constants. This article proposes a methodology to generalize degree, closeness and betweenness centralities taking into account the variability of edge weights in the form of closed intervals (interval-weighted networks, IWN). We apply our centrality measures approach to two real-world IWN. The first is a commuter network in mainland Portugal, between the 23 NUTS 3 Regions. The second focuses on annual merchandise trade between 28 European countries, from 2003 to 2015.

Exposure theory for learning complex networks with random walks

Abstract: Random walks are a common model for the exploration and discovery of complex networks. While numerous algorithms have been proposed to map out an unknown network, a complementary question arises: in a known network, which nodes and edges are most likely to be discovered by a random walker in finite time? Here, we introduce exposure theory, a statistical mechanics framework that predicts the learning of nodes and edges across several types of networks, including weighted and temporal, and show that edge learning follows a universal trajectory. While the learning of individual nodes and edges is noisy, exposure theory produces a highly accurate prediction of aggregate exploration statistics.

Principal centrality measures: a comprehensive approach to the Spanish stocks market

Abstract: In social network analysis, for determining the relevance or significance of a node in the network, several node centrality measures are often used such as degree centrality, betwenness centrality, closeness centrality, eigenvector, subgraph and page rank centrality. In this paper we apply a principal components analysis over the traditional centrality measures for obtaining an overall single metric that combines the best attributes of the traditional centrality measures and permits to detect relevant nodes in the network. Concretely, a detailed study of the Spanish stocks market will be used for demonstrating the advantages of this approach.

Zaproszenie do teorii aktora-sieci – ontologia relacyjna a archeologia

Abstract: Actor-Network Theory and the related ontological turn is one of the most important paradigmatic changes in contemporary social sciences. In Polish archaeological discourse Actor-Network Theory is not widely discussed. The paper presents the basic assumptions of the theory focusing on its potential for the conceptualization of archaeological sources and the relationship between humans and the environment.

The Application of Social Network Theory During a Pandemic

Abstract: This chapter provides a theoretical overview of social network theory, given the widespread of social media during the COVID-19 outbreak. It mainly focuses on three social network theories (the social capital and structural halls, the strength of weak ties, and the small-world). It gives insights into how different researchers have examined these theories during the pandemics and how they have been used in exchanging and communicating information during pandemics. In addition, it reviews previous research concerning how epidemic propagation often happens based on these theories.

Exposure theory for learning complex networks with random walks

Abstract: Random walks are a common model for exploration and discovery of complex networks. While numerous algorithms have been proposed to map out an unknown network, a complementary question arises: in a known network, which nodes and edges are most likely to be discovered by a random walker in finite time? Here we introduce exposure theory, a statistical mechanics framework that predicts the learning of nodes and edges across several types of networks, including weighted and temporal, and show that edge learning follows a universal trajectory. While the learning of individual nodes and edges is noisy, exposure theory produces a highly accurate prediction of aggregate exploration statistics.

Continuous-Time Quantum Walk Based Centrality Testing On Weighted Graphs

Abstract: Abstract Centrality measure is an essential tool in network analysis and widely used in the domain of computer science, biology and sociology. Taking advantage of the speedup offered by quantum computation, various quantum centrality measures have been proposed. However, few work of quantum centrality involves weighted graphs, while the weight of edges should be considered in certain real-world networks. In this work, we extend the centrality measure based on continuous-time quantum walk to weighted graphs. We testify the feasibility and reliability of this quantum centrality using an ensemble of 41675 graphs with various topologies and comparing with the eigenvector centrality measure. The average Vigna’s correlation index of all the tested graphs with all edge weights in [1,10] is as high as 0.967, indicating the pretty good consistency of rankings by the continuous-time quantum walk centrality and the eigenvector centrality. The intuitive consistency of the top-ranked vertices given by this quantum centrality measure and classical centrality measures is also demonstrated on large-scale weighted graphs. Moreover, the range of the continuous-time quantum walk centrality values is much bigger than that of classical centralities, which exhibits better distinguishing ability to pick the important vertices from the ones with less importance. All these results show that the centrality measure based on continuous-time quantum walk still works well on weighted graphs.

Comparative Study of Graph Theory for Network System

Abstract: AbstractThe historical background of how graph theory emerged into world and gradually gained importance in different fields of study is very well stated in many books and articles. Some of the most important applications of graph theory can be seen in the field network theory. Its significance can be seen in some of the complex network systems in the field of biological system, ecological system, social systems as well as technological systems. In this paper, the basic concepts of graph theory in terms of network theory have been provided. The various network models like star network model, ring network model, and mesh network model have been presented along with their graphical representation. We have tried to establish the link between the models with the existing concepts in graph theory. Also, many application-based examples that links graph theory with network theory have been looked upon.KeywordsDegreeMatching indexOperatorsClusterStar network modelMesh network modelRing network model

Disentangling the Effects of Interpersonal and Interfirm Network Centrality on Knowledge Transfer

Abstract: Social network scholars have placed emphasis on the benefits of occupying central network positions on knowledge transfer. Yet, there is a limited understanding on the boundary conditions influencing the relationship between centrality and knowledge transfer. We conduct a meta-analysis to unravel and provide a systematic and comprehensive account of the effects of centrality on knowledge transfer in interfirm and interpersonal networks and identify potential moderators. From meta-analysing 77 empirical studies representing 82 independent samples with 147 effect sizes, we find that the relationship between centrality and knowledge transfer varies depending on the level of analysis but remains consistent across various cultural context. Moreover, the magnitudes of effects of centrality on knowledge transfer significantly differ when diverse centrality types and transferred knowledge dimensions are teased apart, simultaneously examined and compared. To this end, we contribute to social network theory and knowledge-based view by providing a more elaborate account of effects of centrality on knowledge transfer, their boundary conditions, potential theoretical advancements, and managerial implications.