Application Of Network Theory Research Articles

Corporate Governance, Digital Platforms and Network Theory: Information and Risk-Return Sharing of Connected Stakeholders

Digital platforms are a technology-enabled transactional tool that facilitates connections between stakeholders. Their characteristics are consistent with network theory applications where stakeholders are the nodes rotating around the platform. Platforms may be considered a new virtual stakeholder that, consistently with network theory, connects conventional partners (shareholders, managers, employees, lenders, clients, suppliers, etc.), representing a bridging node and edge in multilayer networks. Stakeholders are nodes that interact around the bridging (hub) node, sharing information, and co-creating value within a sustainable digital ecosystem. Shared information is fueled in real-time by big data and reduces asymmetries and risk, redesigning information systems. Corporate governance and managerial implications emerge as a critical, still under-explored issue.

Network structure and risk-based surveillance algorithms for live shrimp movements in Thailand.

Live shrimp movements pose a potential route for site-to-site transmission of acute hepatopancreatic necrosis disease (AHPND) and other shrimp diseases. We present the first application of network theory to study shrimp epizootiology, providing quantitative information about the live shrimp movement network of Thailand (LSMN), and supporting practical and policy implementations of disease surveillance and control measures. We examined the LSMN over a 13-month period from March 2013 to March 2014, with data obtained from the Thailand Department of Fisheries. The LSMN had a mixture of characteristics both limiting and facilitating disease spread. Importantly, the LSMN exhibited power-law distributions of in and out degrees with exponents of 2.87 and 2.17, respectively. This characteristic indicates that the LSMN behaves like a scale-free network and suggests that an effective strategy to control disease spread in the Thai shrimp farming sector can be achieved by removing a small number of targeted inter-site connections (arcs between nodes). Specifically, a disease-control algorithm based on betweenness centrality (defined as the number of shortest paths between node pairs that traverse a given arc) is proposed here to prioritize targets for disease surveillance and control measures.

Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

Neuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often “static” despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time, moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.

Earthquake Networks as a Tool for Seismicity Investigation: a Review

Seismic hazard assessment is one of the main targets of seismological research, aiming to contribute to reducing the catastrophic consequences of strong earthquakes (e.g., \( M \ge 6.0 \)). From the early stage of seismological research, both purely seismological and statistical methods were adopted for seismic hazard assessment. An approach towards this target was attempted by means of network theory, aiming to provide insight into the complex physical mechanisms that cause earthquakes and whether the occurrence of strong earthquakes can be predicted to some extent. Application of network theory in different areas of the world with intense seismic activity, such as Japan, California, Italy, Greece, Iran, and Chile, has yielded promising results that have negligible probability of being obtained by purely random guessing.

A landscape experiment of spatial network robustness and space‐use reorganization following habitat fragmentation

Abstract Network theory increasingly informs wildlife conservation in disturbed landscapes, but with concern increasingly expressed about its application to real‐world situations. The theory predicts that the connectivity of scale‐free networks should be particularly sensitive to the disturbance of highly connected nodes (i.e. hubs). This expectation relies on complete patch removal, thus restraining its application to the last of several steps involved in habitat fragmentation, and overlooks potential reconnection of patches after disturbance (i.e. rewiring). We performed a landscape‐scale experiment to evaluate the robustness of scale‐free spatial networks of woodland caribou (Rangifer tarandus caribou) to logging activity. We built caribou networks before and after disturbance using a mechanistic model of inter‐patch movements and differentiated networks disturbed in their hubs and non‐hubs. We applied a reaction–advection–diffusion model to networks before and after disturbance to account for the spatio‐temporal dynamics of caribou movement within the networks and anticipate their space use. We validated network and space‐use predictions using empirical estimates from GPS relocations of caribou. Using the validated predictions, we compared topological network measures before and after disturbance to quantify changes in connectivity within the networks according to the type of disturbed nodes (i.e. hubs or non‐hubs) and assessed space‐use reorganization. We used control networks, for which no disturbance occurred in the before–after timeframe of the study, in the latter analysis to get a baseline rate of change. Disturbances due to logging activity typically resulted in fragmentation and shrinkage instead of complete patch removal. Independently to the type of disturbed nodes, caribou rewired their network using remnant patches from the fragmentation process. Consequently, topological network measures generally did not differ between before and after disturbance, such that caribou networks displayed some robustness to logging activity due to the rewiring process. Space‐use reorganization was greater, however, when hubs were disturbed in comparison with non‐hubs and controls. Even though caribou rewired their networks, they revisited less patches after the disturbance of hubs. A naive application of network theory (i.e. without potential rewiring and using complete patch removal), to assess spatial network robustness, may be inappropriate during most steps of the fragmentation process because of the rewiring process. Indeed, network rewiring facilitated by the presence of remnant patches can enhance spatial network robustness, in comparison with no rewiring. In addition, species‐specific functional connectivity should be accounted for when anticipating the rewiring process and animal space use within disturbed networks. A free Plain Language Summary can be found within the Supporting Information of this article.

He application of network theory in official statistics

he application of network theory in official statistics

Recent Development in Public Transport Network Analysis From the Complex Network Perspective

A graph, comprising a set of nodes connected by edges, is one of the simplest yet remarkably useful mathematical structures for the analysis of real-world complex systems. Network theory, being an application-based extension of graph theory, has been applied to a wide variety of real-world systems involving complex interconnection of subsystems. The application of network theory has permitted in-depth understanding of connectivity, topologies, and operations of many practical networked systems as well as the roles that various parameters play in determining the performance of such systems. In the field of transportation networks, however, the use of graph theory has been relatively much less explored, and this motivates us to bring together the recent development in the field of public transport analysis from a graph theoretic perspective. In this paper, we focus on ground transportation, and in particular the bus transport network (BTN) and metro transport network (MTN), since the two types of networks are widely used by the public and their performances have significant impact to people's life. In the course of our analysis, various network parameters are introduced to probe into the impact of topologies and their relative merits and demerits in transportation. The various local and global properties evaluated as part of the topological analysis provide a common platform to comprehend and decipher the inherent network features that are partly encoded in their topological properties. Overall, this paper gives a detailed exposition of recent development in the use of graph theory in public transport network analysis, and summarizes the key results that offer important insights for government agencies and public transport system operators to plan, design, and optimize future public transport networks in order to achieve more efficient and robust services.

Functional disorders can also be explained through a non-reductionist application of network theory.

A network structure explains why reductionism is not possible for mental illness, but the same argument applies for the somatic symptoms of functional disorders. Because the covariation of symptoms of functional disorders cannot be explained in terms of symptom-to-symptom causality, explanation requires a network of biological mechanisms having emergent properties that cannot be reduced to biology.

Pure Relationality as a Sociological Theory of Communication

In order to explain the success of populist politicians use of social media, we need to subtract the social from relationality and separate social relationships from network theory applications. A pure theory of relationality is suggested by Werner Heisenberg's breakthrough in quantum mechanics. It is argued that sociology, to its detriment, has failed to incorporate a theory of communication, one adequate to the explosion of social media and the recent rise of populist politics, here instanced by Donald Trump. Realizing the underlying importance of communication technology in all social relationships, and treating these two aspects in a complementary fashion, is the purpose of this essay in sociological theory.

Financial statement networks: an application of network theory in audit

Auditors use predictive models to estimate financial account values in a financial statement. Prior studies suggest that incorporating organizational knowledge into these models yields better predictive accuracy. We propose a novel method to con- struct a financial statement network in order to gain insight into the organizational structure of a company using real financial transaction data from ten companies. We show that real data yields financial statement networks of varying complexity. We introduce a method to aggregate the nodes and edges of the financial statement net- work, which results in its visualization at the right level of tractability. We also show that this visualization enables the auditor to assess the complexity of the organizational structure of a company and to use it as a risk indicator for the audit. Further, the obtained network yields insights into the monetary flow between financial accounts and business processes. We show that this information can be used to add organizational knowledge to predictive models for the purpose of obtaining audit evidence.

Zastosowanie podejścia sieciowego w zarządzaniu portfelem projektów

The increased complexity of projects and the high complexity of managing numerous projects make project portfolio management an area in which network issues still offer inspiring research potential. The theory of organisational networks creates wide possibilities in project portfolio management.The article discusses the application of a network approach in the analysis of project portfolios and indicates new directions in research on project portfolios resulting from the adoption of the network perspective.The article presents three main issues: the concept of the organisational network and its reference to project portfolio management, an interpretation of concepts related to project portfolio management and the network approach to project portfolios. The application of network theory to research on project portfolios, in particular the use of the network analysis method (SNA, or Social Network Analysis), makes it possible to formulate and describe the characteristics of project networks.

Aminopurvalanol A, a Potent, Selective, and Cell Permeable Inhibitor of Cyclins/Cdk Complexes, Causes the Reduction of in Vitro Fertilizing Ability of Boar Spermatozoa, by Negatively Affecting the Capacitation-Dependent Actin Polymerization.

The adoption of high-througput technologies demonstrated that in mature spermatozoa are present proteins that are thought to be not present or active in sperm cells, such as those involved in control of cell cycle. Here, by using an in silico approach based on the application of networks theory, we found that Cyclins/Cdk complexes could play a central role in signal transduction active during capacitation. Then, we tested this hypothesis in the vitro model. With this approach, spermatozoa were incubated under capacitating conditions in control conditions (CTRL) or in the presence of Aminopurvalanol A a potent, selective and cell permeable inhibitor of Cyclins/Cdk complexes at different concentrations (2, 10, and 20 μM). We found that this treatment caused dose-dependent inhibition of sperm fertilizing ability. We attribute this event to the loss of acrosome integrity due to the inhibition of physiological capacitation-dependent actin polymerization, rather than to a detrimental effect on membrane lipid remodeling or on other signaling pathways such as tubulin reorganization or MAPKs activation. In our opinion, these data could revamp the knowledge on biochemistry of sperm capacitation and could suggest new perspectives in studying male infertility.

Taking stock of networks across the security field: a review, typology and research agenda

ABSTRACTSecurity network research has grown considerably in the last decade as it has been increasingly recognised that security is pursued through networks of public, private and hybrid actors or nodes. This research deals with local, institutional, international and virtual security networks and includes techniques such as social network analysis and approaches more familiar to organisational theory and management. However, much of the security network research employs the network concept as a metaphor to suggest a relationship between a set of security nodes, without examining the structural pattern of these relationships or the underlying properties of security networks. Different uses of the network concept have led to confusion about the application of network theory across the security field. This article attempts to address these issues by clarifying the fundamental concepts of a network perspective and revisiting existing typologies of security networks. We review research on the use of network perspectives across the security field, evaluate theoretical and empirical trends, and give directions for future research. We examine the geographical properties of security networks operating at the subnational, national and transnational levels and put forward four types of networks that have the potential to improve security network research: information exchange networks, knowledge generating networks, problem-solving networks and coordination networks. The article concludes by highlighting the importance of networks for understanding and promoting the governance of security.

Spatial connections in regional climate model rainfall outputs at different temporal scales: Application of network theory

Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961–1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network’s local density) and degree distribution (a measure of the network’s spread). The influence of rainfall correlation threshold (T) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are weak, especially when more stringent conditions are imposed (i.e. when T is very high), except at the monthly scale.

Application of network methods for understanding evolutionary dynamics in discrete habitats.

In populations occupying discrete habitat patches, gene flow between habitat patches may form an intricate population structure. In such structures, the evolutionary dynamics resulting from interaction of gene-flow patterns with other evolutionary forces may be exceedingly complex. Several models describing gene flow between discrete habitat patches have been presented in the population-genetics literature; however, these models have usually addressed relatively simple settings of habitable patches and have stopped short of providing general methodologies for addressing nontrivial gene-flow patterns. In the last decades, network theory - a branch of discrete mathematics concerned with complex interactions between discrete elements - has been applied to address several problems in population genetics by modelling gene flow between habitat patches using networks. Here, we present the idea and concepts of modelling complex gene flows in discrete habitats using networks. Our goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats. We review the main branches of network theory that have been, or that we believe potentially could be, applied to population genetics and molecular ecology research. We address applications to theoretical modelling and to empirical population-genetic studies, and we highlight future directions for extending the integration of network science with molecular ecology.

Exotic phase transitions of k-cores in clustered networks.

The giant k-core-maximal connected subgraph of a network where each node has at least k neighbors-is important in the study of phase transitions and in applications of network theory. Unlike Erdős-Rényi graphs and other random networks where k-cores emerge discontinuously for k≥3, we show that transitive linking (or triadic closure) leads to 3-cores emerging through single or double phase transitions of both discontinuous and continuous nature. We also develop a k-core calculation that includes clustering and provides insights into how high-level connectivity emerges.

USING NETWORK THEORY TO DETECT DOMINANT CARTEL FIRMS

Despite multiple applications of network theory in different fields of social and legal sciences in general, the possibility of applying this theory to the economic analysis of the antitrust law and, more specifically, to the study of cartels has not yet been considered. Nevertheless, the study of cartels as networks can advance some general elements and measurements that can help the detection and elimination of these organizational forms that cause evident harm to general well-being. This is the first time that the approach of network theory has been used to analyze cartels. This article uses a set of classical measures such as clustering and centrality measures, taken from network theory, and applies them to a specific case of a cartel sanctioned as such by the European Commission. This approach has enabled us to quantify some characteristic elements of the cartel, such as, for instance, a remarkable asymmetry between operators (nodes in the network), its different degree of influence (study of links), as well as the critical importance of some operators versus other cartelized agents, such that their elimination from the organization would not enable them to create their own cartel. This finding warrants reconsidering the antitrust policy based on leniency programs.

Approximating frustration scores in complex networks via perturbed Laplacian spectra.

Systems of many interacting components, as found in physics, biology, infrastructure, and the social sciences, are often modeled by simple networks of nodes and edges. The real-world systems frequently confront outside intervention or internal damage whose impact must be predicted or minimized, and such perturbations are then mimicked in the models by altering nodes or edges. This leads to the broad issue of how to best quantify changes in a model network after some type of perturbation. In the case of node removal there are many centrality metrics which associate a scalar quantity with the removed node, but it can be difficult to associate the quantities with some intuitive aspect of physical behavior in the network. This presents a serious hurdle to the application of network theory: real-world utility networks are rarely altered according to theoretic principles unless the kinetic impact on the network's users are fully appreciated beforehand. In pursuit of a kinetically interpretable centrality score, we discuss the f-score, or frustration score. Each f-score quantifies whether a selected node accelerates or inhibits global mean first passage times to a second, independently selected target node. We show that this is a natural way of revealing the dynamical importance of a node in some networks. After discussing merits of the f-score metric, we combine spectral and Laplacian matrix theory in order to quickly approximate the exact f-score values, which can otherwise be expensive to compute. Following tests on both synthetic and real medium-sized networks, we report f-score runtime improvements over exact brute force approaches in the range of 0 to 400% with low error (<3%).

Networked Learning and Network Science: Potential Applications to Health Professionals' Continuing Education and Development.

Prior interpersonal relationships and interactivity among members of professional associations may impact the learning process in continuing medical education (CME). On the other hand, CME programs that encourage interactivity between participants may impact structures and behaviors in these professional associations. With the advent of information and communication technologies, new communication spaces have emerged that have the potential to enhance networked learning in national and international professional associations and increase the effectiveness of CME for health professionals. In this article, network science, based on the application of network theory and other theories, is proposed as an approach to better understand the contribution networking and interactivity between health professionals in professional communities make to their learning and adoption of new practices over time.

Networks, Cartels, and Antitrust Policy

Despite multiple applications of network theory in different fields of social and legal sciences in general, the possibility of applying this theory to the economic analysis of the antitrust law and, more specifically, to the study of cartels has not yet been considered. This paper develops a set of distances, clustering and centrality measures, taken from network theory, and applies them to the specific case of a cartel sanctioned as such by the European Commission. This approach has enabled us to quantify some characteristic elements of the cartel, such as, for instance, a remarkable asymmetry between operators (nodes in the network), its different degree of influence (study of links), as well as the critical importance of some operators versus other cartelized agents, such that their elimination from the organization would not enable them to create their own cartel. This leads the authors to reconsider the antitrust policy based on leniency programmes.