Network Interconnectedness Research Articles

Complex-Systems Analysis of the CSI 300 Index: Evolution, Resilience, and Prediction in Stock Correlation Network

With the outbreak and evolution of the pandemic worldwide, the financial market has experienced unprecedented shocks and adjustments, and the volatility and correlation of the stock market, as an important indicator of economic activities, have shown new features and trends during the pandemic. Based on the CSI 300 Index, we construct a three-stage sequential network representing the pre-pandemic, pandemic, and post-relaxation phases. We investigate the evolving dynamics and resilience of the network, forecasting potential future connections, thus offering fresh insights into comprehending market recovery. Our findings unveil that the market adapts dynamically to the pandemic’s progression, witnessing an overall augmentation in network interconnectedness. While the financial sector maintains its pivotal role, the influence of non-financial sectors experiences an upsurge. Despite the network demonstrating poor stability and heavy reliance on key nodes, there exists a positive recovery trajectory. Non-financial sectors such as energy and transportation emerge as pivotal catalysts for market rejuvenation. We provide suggestions for government regulators and investors, providing strong support for optimizing the market structure and promoting the long-term healthy development of the market.

Energy expansion planning with a human evolutionary model

This study presents a novel method for planning the expansion of transmission lines and energy storage systems while considering the interconnectedness of electricity and gas networks. We developed a two-level stochastic planning model that addresses both the expansion of transmission and battery systems in the electrical grid and the behavior of the gas network. This research explores the challenges and effects of integrating high levels of renewable energy sources while ensuring security within both networks. Our model uses a stochastic mixed-integer non-linear programming approach. To solve this complex model, we applied the Human Evolutionary Model (HEM). We tested our approach on two case studies: a simple 6-node network and the more complex IEEE RTS 24-bus network for the electricity grid, combined with 5-node and 10-node gas networks, respectively. The results demonstrate the effectiveness of our model, particularly in scenarios where connections in the power and gas networks are disrupted, preventing load shedding even when integrated network connections are cut.

Impact of low-carbon monetary policies on climate-related systemic risk: Evidence from China

The issue of using green finance policies to prevent climate-related systemic risk (CRSR) while promoting low-carbon production is of great concern. Despite its significance for a stable transition to low-carbon energy, this topic remains underexplored in the literature. Our study examines the impact of monetary policies promoting low-carbon development on CRSR based on China's low-carbon transition scenario. To achieve this, we develop a financial network model with a forward-looking climate stress-testing framework. This model captures banks' heterogeneous behaviors regarding carbon asset allocation and the systemic feedback effects of climate policy shocks in a multilayer network. Our findings reveal that targeted reductions in the required reserve ratio (RRR) and carbon reduction re-lending policies have significant and distinct effects on CRSR. We also observe that larger banks tend to have higher systemic importance and are more responsive to adjustments in low-carbon monetary policies. By contrast, relatively smaller banks demonstrate greater vulnerability to climate policy shocks. Additionally, these policies influence CRSR through mechanisms involving credit restructuring and interbank network interconnectedness. This study provides valuable insights for enhancing low-carbon financial policies and facilitating a stable low-carbon transition.

Sensitivity analysis of shock distributions in the world economy.

With the ever increasing interconnectedness among countries and industries, globalization has empowered economies and promoted international trade, capital flow and labor mobility, leading to improved products and services. However, the growing interdependence has also propelled an inherent reliance on joint cooperation which has considerably influenced the complexity of global value chains (GVCs). This plays a significant role in policy decisions, raising questions about trade risks that originate from such interdependence. In this paper, we study the impact of network linkage disturbances on the output supply and input demand of countries. We model the network interconnectedness of countries according to the latest 2016 release of the World Input-Output Database (WIOD) that includes data tables for the period 2000-2014 covering 43 countries as well as a model for the Rest of the World (ROW). We assess the shock distributions across the world economy by quantifying the changes in the network linkages using sensitivity analysis. Our contribution is in the definition of a shock tensor with the purpose of evaluating the impact of link sensitivity. The shock tensor is a straightforward yet comprehensive tool that allows us to obtain ample results at various levels of granularity when combining it with aggregation operators. Our study introduces a novel methodology that enables us to acquire input and output link sensitivities for all country pairings when an economic shock initiates or concludes within a country of interest. This innovative approach also facilitates the analysis of evolving trends in these link sensitivities, providing a comprehensive understanding of the dynamics of shock propagation across the global network. Taking advantage of the time-series nature of the WIOD, our results reveal illustrative visualizations and quantative measures that characterize patterns of shock distribution and relationships among countries throughout the period from 2000 to 2014. Our methodology and results not only uncover valuable trends but also establish a structured approach to better understand the aggregate effects of shock distributions. Thus, this study could be helpful for policy makers to assess trade relationships between countries and obtain quantitative insights for making informed decisions as well as explore the overall state of the globalization as a whole.

Bank stability in the uncollateralised overnight interbank market: A topological analysis

We study the topology characteristics of the Kenyan overnight interbank market and their impacts on bank stability. Our intraday transaction dataset covers 2003 to 2012, including six major liquidity shocks. We uncover new results that the Kenyan interbank network is an incomplete network with higher interconnectedness and exposure during liquidity shocks, such that the shocks tend to spread quickly throughout the network. The main implication of our finding is that in such tiered networks, core banks could pose risks to the whole system. Consistently, our further empirical results suggest that the high interbank network interconnectedness can smoothen liquidity flow during quiet times, but may lead to over-exposure to borrowing banks directly or indirectly, especially during disturbances.

The effect of interconnectivity on stock returns during the Global Financial Crisis

This paper examines the role of interconnectivity in global stock markets during the Global Financial Crisis (GFC) using a comprehensive dataset of 8,827 firms traded in developed and emerging markets. Our contribution includes two key findings. We first use a difference-in-differences approach to show that stocks in countries with higher trade openness ex-ante to the GFC experienced lower average and annual cumulative returns of 9.08 p.p. and 36.32 p.p., respectively, compared to stocks traded in less exposed countries, one year after the crisis outbreak. Second, we employ complex network theory to analyze the role of network interconnectedness in our baseline results. To construct the network of interdependence between stock returns, we utilize a regularized Vector Autoregression Model, which enables us to overcome the limitations of commonly used correlation networks. Our findings suggest that while a firm’s high connectivity before the crisis can alleviate adverse shock effects resulting from export dependence, this effect may be weakened if the firm’s performance is closely linked to central firms.

Business cycle asymmetry and input-output structure: The role of firm-to-firm networks

We study the network origins of business cycle asymmetries using cross-country and administrative firm-level data. At the country level, we document that countries with a larger number of non-zero intersectoral linkages (denser networks) display a more negatively skewed cyclical component of output. At the firm level, we find that firms with a larger number of suppliers and customers (degrees) display a more negatively-skewed distribution of their output growth. To rationalize these findings, we construct a multisector model with input-output linkages and show that the relationship between output skewness and network density naturally arises once we consider non-linearities in production. In an economy with low production flexibility (inputs are gross complements), denser production structures imply that relying on more inputs becomes a risk that further amplifies the effects of negative productivity shocks. The opposite holds if firms display high production flexibility (inputs are gross substitutes): having more inputs to choose from becomes an opportunity to diversify the effects of negative productivity shocks. We calibrate the model using our rich firm-to-firm network Chilean data and show that more connected firms experience larger declines in output in response to a COVID-19 shock, consistent with the data. We also show that, as in the data, the cross-sectional distribution of output growth in the model displays a fatter left tail during downturns. The previous result is shaped by the interplay between production complementarities and network interconnectedness, rather than by the asymmetry of the shocks. The size of the shock determines the strength of the relationship between degrees and output decline, which highlights the importance of non-linearities and the limitations of local approximations.

Choosing Where to Fight: Do Social Networks Distinguish American ISIS Foreign Fighters from ISIS-Inspired Terrorists?

Why did some American citizens choose to travel to fight in Syria and Iraq rather than engage in Islamic State of Iraq and Syria (ISIS)-inspired terrorism in the United States? We conducted a social network analysis (SNA) on a sample ( n = 224) of extremists who either plotted ISIS-inspired attacks within the United States or attempted to travel to Syria or Iraq to join the group between 2013-2020. We test how network size, network interconnectedness, and the importance of trusted network members impact the choice of American ISIS offenders to travel or plot terrorist attacks. Our results show that Americans were more likely to choose to travel to fight when they had access to large, dense networks that were embedded with trusted associates. Those without access to similar networks abandoned their preferences for foreign fighting and instead plotted attacks within the United States. The findings provide pertinent policy implications for countering violent extremism.

Forecasting Chinese macroeconomy with volatility connectedness of financial institutions

ABSTRACT Systemic risk emphasizes the impact on the real economy and is popularly measured by a network interconnectedness approach. We test, for the first time, whether the volatility connectedness of financial institutions is a significant predictor of Chinese macroeconomy. The connectedness is derived from volatility spillover networks and is measured by total connectedness introduced in Diebold and Yilmaz (2014), which reflects the effects of risk transmission and systemic risk in the financial system. Both in-sample and out-of-sample analyses show that an increase in total connectedness among financial institutions stably and strongly forecasts a slowdown in China’s economic activity over the next three to twelve months, when controlling for many factors. Furthermore, including the total connectedness into the regression models improves the macroeconomy forecasts accuracy. Our results are robust to alternative measures of total connectedness.

Evaluating the Interdependencies of Infrastructure Critical Systems during Earthquake Event: A Case Study for Padang City

Our modern society is becoming increasingly reliant on transportation networks, as well as the interdependent infrastructures and technologies that interact with them. The increasing complexity and interconnectedness of infrastructure networks make them susceptible to impact not only directly from external shocks but also indirectly from the failure of dependent infrastructures. This research study was conducted in Padang city, one of the most disaster-prone areas in Indonesia. Based on the literature review, it is no doubt that research study on seismic risk assessment is insufficient and outdated. In fact, a study about the interdependency between Critical Infrastructures (CIs) is yet to be done in this region. In this study, there are two approaches used for data gathering which is by surveying existing CIs using Google Earth and by an online questionnaire survey via Google Form. Based on the qualitative survey, a functionality rating method is done to obtain the level of outage/loss functionality which is an indicator for the damage occurred to the structure and infrastructure. Following that, a seismic risk analysis was conducted to assess the interdependency between investigated CIs and facilities. Respondents’ judgement from the questionnaire were used to identify the base criticality of each critical infrastructure. Based on the qualitative survey, the level of loss in functionality for the substation and the telecommunication tower is rated as “High”, but the loss in functionality for the water supply system is rated as “Moderate”. Moreover, the findings used from the respondents’ judgements were used to establish the initial level of criticality for each vital infrastructure. According to the findings, hospitals, power substations, and communication towers all have a criticality level of “5-Vital”, while police stations and fire stations both have a “3-medium” criticality rating. Eventually, the results of this assessment of interdependence are displayed in a criticality map, which shows how the interdependency relationship affects the initial criticality of a certain upstream infrastructure. Understanding the potential consequences of infrastructure failure, especially in regard to dependent infrastructures, can help emergency response teams formulate more targeted strategies for managing risks. As a consequence of this, the resilience of the wider community is improved, which contributes toward the implementation of Sustainable Development Goal (SDG) 11: Sustainable cities and communities particularly in reducing disasters and people in vulnerable situation.

Cognitive and Structural Embeddedness of the Supply Base: Effects on Buyer Profitability

Recent supply chain operations studies have made strides in understanding the structural and relational embeddedness of firms in the supply base and their impact on the performance of the focal buyer. Yet, the theory on cognitive and cultural embeddedness remains unclear. Understandably, cognitive and cultural embeddedness of firms are less observable. Indeed, cognitive and cultural embeddedness explains the deep motivations of firms’ actions - why firms do certain things and why they pursue certain kinds of relationships in the first place. This study takes an incremental step in this direction. We propose and empirically examine the relationships between shared cognition among firms in a supply base and the financial performance of the focal buying firm. Through analysis of a sample of 240 ego-networks of buying firms, our overall theoretical argument is supported, and we find a nuanced understanding of how such network-shared cognition functions. More specifically, in a supply base, cognition shared among a buyer and suppliers is positively associated with the buyer’s financial performance (ROA). However, when the network becomes cognitively homogeneous, it has a detrimental effect on the buyer’s financial performance, revealing the “dark side” of cognitive embeddedness. We also identified the moderating effect of network interconnectedness, which allows the buyer to benefit from suppliers’ network shared cognition. That is, only in the presence of a very interconnected network, the potential benefits from supplier-supplier shared cognition will materialize for the buyer; otherwise, its potential remains dormant.

A Deep Learning Approach to Dynamic Interbank Network Link Prediction

Lehman Brothers’ failure in 2008 demonstrated the importance of understanding interconnectedness in interbank networks. The interbank market plays a significant role in facilitating market liquidity and providing short-term funding for each other to smooth liquidity shortages. Knowing the trading relationship could also help understand risk contagion among banks. Therefore, future lending relationship prediction is important to understand the dynamic evolution of interbank networks. To achieve the goal, we apply a deep learning framework model of interbank lending to an electronic trading interbank network for temporal trading relationship prediction. There are two important components of the model, which are the Graph convolutional network (GCN) and the Long short-term memory (LSTM) model. The GCN and LSTM components together capture the spatial–temporal information of the dynamic network snapshots. Compared with the Discrete autoregressive model and Dynamic latent space model, our proposed model achieves better performance in both the precrisis and the crisis period.

Towards a new model of EU-China innovation cooperation: Bridging missing links between international university collaboration and international industry collaboration

This paper theoretically conceptualises and empirically explores the role of international university collaboration in building transnational university–industry co-innovation networks (TUICNs) in the EU–China context, and suggests a new model of international innovation cooperation. The theoretical framework was constructed by synthesising insights from social network theory and institutional theory. The empirical exploration was based on an analysis of interviews with 18 actors engaged in EU–China innovation cooperation. The findings suggest that international university collaboration could contribute to international industry collaboration and, therefore, to the development of TUICNs by 1) matching suitable industrial partners, 2) building trust between them, 3) giving industrial actors access to new resources, (4) enhancing the reputations of international companies in their cooperation countries, and (5) creating innovative business collaboration models. The study also addresses the following paradox in innovation studies: although the vital role of universities in national/regional innovation systems has been widely studied and the global interconnectedness of innovation networks is generally acknowledged, little attention has been given to universities’ engagement in transnational innovation (eco)systems. Based on the findings, recommendations for policymakers, universities and companies are provided in light of the shifting balance of challenges and opportunities presented by China to the EU.

Fueling green connections: Networked policy instrument choices for sustainability regulation

AbstractThis paper presents a policy network analysis study that examines policy subsystem interconnectedness and cohesion, to explore three constituent regulatory policy instruments that have a combined impact on governing sustainability. The network surrounding the environmental sustainability of biodiesel policy in Indonesia is marked by high interconnectedness and relatively weaker cohesion, properties that impact how policy solutions are formulated through policy actor interactions and define prevailing policy formulation styles. Using primary policy network data on 46 organizations and the review of national regulations, laws, and directives, this paper tests established hypotheses on how instruments that are formulated within the same policy subsystem, share similar characteristics. While overall network interconnectedness and cohesion are important first indicators of policy instrument selection in the literature on policy design, this article recommends also including an examination of network centrality and the characteristics of dominant central actors who are in favorable positions to design policies, to better accommodate for different policy formulation styles.

Understanding the uneven spread of COVID-19 in the context of the global interconnected economy

The worldwide spread of the COVID-19 pandemic is a complex and multivariate process differentiated across countries, and geographical distance is acceptable as a critical determinant of the uneven spreading. Although social connectivity is a defining condition for virus transmission, the network paradigm in the study of the COVID-19 spatio-temporal spread has not been used accordingly. Toward contributing to this demand, this paper uses network analysis to develop a multidimensional methodological framework for understanding the uneven (cross-country) spread of COVID-19 in the context of the globally interconnected economy. The globally interconnected system of tourism mobility is modeled as a complex network and studied within the context of a three-dimensional (3D) conceptual model composed of network connectivity, economic openness, and spatial impedance variables. The analysis reveals two main stages in the temporal spread of COVID-19, defined by the cutting-point of the 44th day from Wuhan. The first describes the outbreak in Asia and North America, the second stage in Europe, South America, and Africa, while the outbreak in Oceania intermediates. The analysis also illustrates that the average node degree exponentially decays as a function of COVID-19 emergence time. This finding implies that the highly connected nodes, in the Global Tourism Network (GTN), are disproportionally earlier infected by the pandemic than the other nodes. Moreover, countries with the same network centrality as China are early infected on average by COVID-19. The paper also finds that network interconnectedness, economic openness, and transport integration are critical determinants in the early global spread of the pandemic, and it reveals that the spatio-temporal patterns of the worldwide spreading of COVID-19 are more a matter of network interconnectivity than of spatial proximity.

A new approach for node centrality evaluation based on residual network performance measurement

SummaryThe identification of the most influential nodes has been a vibrant subject in complex network research, with many applications across informational, social or biological networks. Despite substantial empirical studies, there is still some defect in the traditional approach for evaluating nodes centralities. The traditional approach for evaluating nodes centralities is to measure network performance change before and after a node fails. To be compatible with this approach, network performance measurement indexes should be monotonic and adequate, which is a constraint condition too strong to satisfy. As a result, many indexes were actually abused in some topologies of networks and the ranking result of nodes centralities may be unreasonable in some application scenarios. To solve the problem, the fundamental rationale of this approach is studied as well as its theoretical defect. Then, a new approach is proposed for evaluating node centrality based on measurement of residual network performance after a node fails. Furthermore, a new index called comprehensive distance is presented for measuring network interconnectedness whether cascading failure is considered or not, which is suitable for not only connected networks but also unconnected networks. Finally, some cases are used to test the validity of the proposed approach. By comparison, the ranking results of nodes centralities evaluated by the proposed approach are more accurate than the results evaluated by the traditional approach. The study about how to measure performance of residual network not only advances the field in terms of node centrality evaluation but also promotes the state of the art in recent related fields such as neural network research and application.

Major customer network structure and supplier trade credit

PurposeThis study investigates how a firm's structural embeddedness, the structural position in a supply network that consists of major customers, influences the acquisition of supplier trade credit. Specifically, this study examines how network interconnectedness, network integration and network independence of a firm affect its ability to acquire supplier trade credit.Design/methodology/approachThis study utilizes financial data from Compustat to build a longitudinal dataset of manufacturing firms from 1998 to 2013. Customer segment disclosure data are used to construct firm-level network variables. A fixed effect regression approach is used for estimation.FindingsThe study results show that network interconnectedness is negatively associated with supplier trade credit, while network integration is positively associated with supplier trade credit. Network independence does not influence the extent of supplier trade credit. The post hoc analysis shows that the effects of the hypothesized factors vary under different product categories and credit ratings.Originality/valueThis study broadens the supply chain finance literature by showing how a firm's embedded network structural position can influence its ability to obtain supplier trade credit.

Subgrain Boundaries as Sites of Melt Generation and Pathways for Melt Flow: Implications for Rheology From Combined Chemical and Quantitative Orientation Analysis

AbstractWe report a detailed petrographic, compositional, structural, and crystallographic investigation into the partial melting process. Migmatitic samples that record biotite‐dehydration melting in the Wet Mountains of central Colorado preserve textural evidence for partial melting locations and an incipient former melt network. Detailed analysis of partial melting locations was conducted using a combined energy dispersive X‐ray spectroscopy (EDS) and electron backscatter diffraction (EBSD) approach. Cuspate‐shaped films of former melt, now plagioclase, with low apparent dihedral angles and adjacent to rounded and corroded reactant minerals are interpreted as former locations of partial melting. We identified former melt films infiltrating and melting along quartz grains using optical microscopy and EDS and determined crystallographic misorientation across these transects using EBSD. Forty‐two percent of these transects are associated with subgrain boundaries, and the remaining transects are between adjacent quartz grains with various misorientations. Although previous work has emphasized grain boundaries and triple junctions as sites for melting, the presence of over 40% of the former melt films developed along subgrain boundaries suggests that these are favorable and potentially preferred locations for partial melting. Consequently, melting along subgrain boundaries must be integral to the evolution of partial melt networks and will increase melt network interconnectedness and generate a finer grain size, enhancing permeability and leading to focused melt flow. Given that these factors exert a fundamental control on crustal rheology, the role of melting and migration along subgrain boundaries should be investigated within diverse crustal and mantle lithologies and incorporated into models of partial melting.

Effects of innovation modes and network partners on innovation performance of young firms

PurposeWhile the importance of “science and technology-based innovation” (STI) and “doing, using and interacting-based innovation” (DUI) innovation modes in firm innovation performance has been well-established, little is known about how they affect the innovation performance of young firms. The author examines the most effective innovation mode and boundary conditions for the innovation performance of developing companies.Design/methodology/approachThe author tests the two modes of innovation using data from 159 young firms in China.FindingsThe author’s analysis indicates that a higher level of DUI innovation mode is more relevant to the innovation performance of newly established enterprises. Moreover, the effectiveness of the innovation mode is bound by the networks in which the company operates and interacts. The effectiveness of STI and DUI innovation modes is enhanced when there are high levels of innovation and business network interconnectedness from the technology network partner.Research limitations/implicationsThese findings have important implications on innovation research as they highlight the joint effects of innovation modes and quality of network ties on young firms seeking to improve their innovation performance.Practical implicationsSTI and DUI innovation modes represent different forms of innovation activities that may affect the knowledge and resources of young firms used to improve innovation performance. Knowing this can help young firms to choose effective innovation mode.Originality/valueThis study makes three contributions. The first is to pay specific attention to the neglected topic of the influence of STI and DUI innovation modes on innovation performance of young firms. Understanding that the two innovation modes offer different methods of gaining knowledge and resources can help young firms choose an effective innovation mode for their business; Second, the author examines the boundary conditions of the effectiveness of innovation modes. Specially, the author examines the moderating role of external networks, which can help clarify conflicting results in this regard. 10;The third contribution is to investigate the importance of network relationships for innovation activities moving beyond the extent of network relationships to instead consider the ability of those relationships to expose a firm to innovative techniques and methods.

Policing Cyberspace: Understanding Online Repression in Thailand and the Philippines

Social networking sites have become increasingly relevant in the study of democracy and culture in recent years. This study explores the interconnectedness of social networks, the imposition of state control, and management of social behavior by comparing various literature on the operation of repression in Thai and Philippine cyberspaces. It examines the overt and covert policing of daily interactions in digital environments and unpacks governmental technologies’ disciplinary mechanisms following Michel Foucault’s notion of government and biopolitical power. Subjugation in the context of social networks merits analysis for it sheds light on the practice of active and passive self-censorship—the former driven by the pursuit of a moral self-image and the latter by state-sponsored fear. In tracing various points of convergence and divergence in the practice of cyber control in Thailand and the Philippines, the study found newer domains of regulation of social behavior applicable to today’s democracies.