Network Perspective Research Articles

Exploring Innovative Structures and Mechanisms in Urban Grassroots Governance: A Network Governance Perspective

This paper examines the "Five Chiefs System" as a grassroots governance model in Chongqing, China, exploring innovative mechanisms in urban grassroots governance from a networked governance perspective. Through a literature review, this study summarizes existing research on innovation in urban grassroots governance, highlighting approaches in technological governance, process re-engineering, and structural research. Building on theories of governance and networked governance, and using the "Five Chiefs System" in Chongqing as a case study, the paper reveals discrepancies between the system and its operational goals as well as the principles of co-governance in practice. Consequently, an analysis of the adaptability of the "Five Chiefs System" and networked governance leads to the exploration of innovative structures and mechanisms for the networked governance of the "Five Chiefs System." This involves developing a decentralized network structure with diverse entities and establishing mechanisms for integration, trust, coordination, and maintenance within networked governance.

Cryptocurrency: A new player or a new crisis in financial markets? —— Evolutionary analysis of association and risk spillover based on network science

In recent years, cryptocurrency has attracted widespread research attention. Against the backdrop of financial globalization, the high volatility of cryptocurrency prices may pose a threat to the stability of the entire financial system. To explore the potential association and transmission of financial risks between cryptocurrency and traditional financial assets, this paper constructs a complex network using the Granger causality method and the DY spillover method. Based on data from 24 categories of assets or index returns from different markets, networks of association and risk spillover between cryptocurrency market and traditional financial markets are established under different time periods, the study covers the period from January 2015 to November 2023. Analyzing the mutual influence between cryptocurrency and traditional financial markets from the perspective of complex networks, and evaluating the market positioning, risk characteristics, and investment potential of cryptocurrency. Research findings indicate that cryptocurrency possesses not only monetary characteristics but also functions as an investment tool, significantly impacting the stability of traditional financial markets. Furthermore, different types of cryptocurrencies vary in market functions, risk characteristics, and investment potentials. Future research could further explore the nonlinear and dynamic associations between cryptocurrency and traditional financial markets, as well as the micro mechanisms and behavioral factors within cryptocurrency markets.

A Cognitive Network Perspective on Creativity: Theorizing Network Mobilization Scripts

A Cognitive Network Perspective on Creativity: Theorizing Network Mobilization Scripts

Network intervention analysis indicates that roluperidone achieves its effect on negative symptoms of schizophrenia by targeting avolition

Roluperidone, a 5-HT2A, sigma2, and ɑ1A-adrenergic receptor antagonist, has proven efficacious for treating negative symptoms of schizophrenia in phase 2b and phase 3 clinical trials. Using network analysis, we demonstrated that the improvements observed in the phase 2b trial resulted from targeting avolition which was highly central and spurred a cascading effect of global negative symptom reductions when successfully treated. The current study aims to replicate these network findings using the phase 3 roluperidone clinical trial data. Participants included 496 schizophrenia patients with moderate to severe negative symptoms who were randomized to either roluperidone 32 mg/day (n =167), 64 mg/day (n = 162), or placebo (n = 167). Negative symptoms were assessed at baseline and weeks 2,4,8, and 12. Network intervention analysis (NIA) evaluated treatment-induced symptom changes over time to identify direct and indirect treatment effects. This analytic approach extends prior work by determining whether the symptoms with highest centrality have causal effects on the entire negative symptom construct and directly lead to symptom improvement. NIA indicated that the efficacious 64 mg/day dose of roluperidone had a direct effect on avolition, suggesting that changes in avolition propels treatment effects across the entire negative symptom constellation. These phase 3 findings replicated the phase 2b findings, indicating that from a network perspective, roluperidone achieves its effect by influencing the extent to which avolition drives other negative symptoms. These findings are relevant for understanding negative symptoms and how to treat them in neuropsychiatric disorders.

Dynamic network analysis of electrophysiological task data

Abstract An important approach for studying the human brain is to use functional neuroimaging combined with a task. In electrophysiological data, this often involves a time-frequency analysis, in which recorded brain activity is time-frequency transformed and epoched around task events of interest, followed by trial-averaging of the power. While this simple approach can reveal fast oscillatory dynamics, the brain regions are analysed one at a time. This causes difficulties for interpretation and a debilitating number of multiple comparisons. In addition, it is now recognised that the brain responds to tasks through the coordinated activity of networks of brain areas. As such, techniques that take a whole-brain network perspective are needed. Here, we show how the oscillatory task responses from conventional time-frequency approaches can be represented more parsimoniously at the network level using two state-of-the-art methods: the HMM (Hidden Markov Model) and DyNeMo (Dynamic Network Modes). Both methods reveal frequency-resolved networks of oscillatory activity with millisecond resolution. Comparing DyNeMo, HMM, and traditional oscillatory response analysis, we show DyNeMo can identify task activations/deactivations that the other approaches fail to detect. DyNeMo offers a powerful new method for analysing task data from the perspective of dynamic brain networks.

Evolution and influential factors of inbound tourist flow network in China: a two-mode network perspective

ABSTRACT Different from existing studies focusing on tourist flow networks at the destination level, this study adopts a two-mode network analysis to identify the network structure, market groupings, and evolution of inbound tourist flows from 13 major origin countries to 31 destination provinces (municipalities, autonomous regions) in China. Furthermore, the quadratic assignment procedure (QAP) method is applied to analyse the influential factors of the formation of market groupings in the network. The results show that: (1) The connections within the destination-destination network and the origin-origin country network show a fluctuating increase trend. (2) Eastern provincial destinations occupy important positions in the network, and central and western destinations have played increasingly important roles in recent years. (3) Japan, South Korea, and the United States are the most important origin countries, and markets in Europe and Oceania have great potential. (4) Two market groupings led by Russia and Asian countries are relatively stable, while other origin countries have no specific regional preference. (5) Geographical distance exerts the most significant impact on the formation of market groupings, followed by tourism resources, economic development, and traffic accessibility.

Network analysis of trait aggression among community youths and juvenile offenders

ObjectiveThe mainstream view in trait aggression research has regarded the structure as representing the latent cause of the cognitions, emotions, and behaviors that supposedly reflect its nature. Under network perspective, trait aggression is not a latent cause of its features but a dynamic system of interacting elements. The current study uses network theory to explain the structure of relationships between trait aggression features in juvenile offenders and their peers.MethodsNetwork analysis was applied to investigate the dynamic system of trait aggression operationalized by the Buss-Perry Aggression Questionnaire in a sample of community youths (Mage = 17.46, N = 715) and juvenile offenders (Mage = 18.36, N = 834).ResultsThe facet level networks showed that anger is a particularly effective mechanism for activating all other traits. In addition, anger was more strongly associated with physical aggression and the overall network strength was greater in juvenile delinquency networks than in their peers. The item level networks revealed that A4 and A6 exhibited the highest predictability and strength centrality in both samples. Also, the Bayesian network indicated that these two items were positioned at the highest level in the model. There are similarities and differences between juvenile delinquents and community adolescents in trait aggression.ConclusionTrait aggression was primarily activated by difficulty controlling one’s temper and feeling like a powder keg.

Network analysis of gut microbial communities reveal key genera for a multiple sclerosis cohort with Mycobacterium avium subspecies paratuberculosis infection

BackgroundIn gut ecosystems, there is a complex interplay of biotic and abiotic interactions that decide the overall fitness of an individual. Divulging the microbe-microbe and microbe-host interactions may lead to better strategies in disease management, as microbes rarely act in isolation. Network inference for microbial communities is often a challenging task limited by both analytical assumptions as well as experimental approaches. Even after the network topologies are obtained, identification of important nodes within the context of underlying disease aetiology remains a convoluted task. We therefore present a network perspective on complex interactions in gut microbial profiles of individuals who have multiple sclerosis with and without Mycobacterium avium subspecies paratuberculosis (MAP) infection. Our exposé is guided by recent advancements in network-wide statistical measures that identify the keystone nodes. We have utilised several centrality measures, including a recently published metric, Integrated View of Influence (IVI), that is robust against biases.ResultsThe ecological networks were generated on microbial abundance data (n = 69 samples) utilising 16 S rRNA amplification. Using SPIEC-EASI, a sparse inverse covariance estimation approach, we have obtained networks separately for MAP positive (+), MAP negative (-) and healthy controls (as a baseline). Using IVI metric, we identified top 20 keystone nodes and regressed them against covariates of interest using a generalised linear latent variable model. Our analyses suggest Eisenbergiella to be of pivotal importance in MS irrespective of MAP infection. For MAP + cohort, Pyarmidobacter, and Peptoclostridium were predominately the most influential genera, also hinting at an infection model similar to those observed in Inflammatory Bowel Diseases (IBDs). In MAP- cohort, on the other hand, Coprostanoligenes group was the most influential genera that reduces cholesterol and supports the intestinal barrier.ConclusionsThe identification of keystone nodes, their co-occurrences, and associations with the exposome (meta data) advances our understanding of biological interactions through which MAP infection shapes the microbiome in MS individuals, suggesting the link to the inflammatory process of IBDs. The associations presented in this study may lead to development of improved diagnostics and effective vaccines for the management of the disease.

Social contagions in business resilience: Evidence from the U.S. restaurant industry in the COVID-19 pandemic

The unprecedented COVID-19 has led to the collapse of numerous businesses, notably within the tourism and hospitality sectors. Despite the burgeoning research on resilience, few studies have embraced a theoretical lens, particularly from a social network perspective. In addition, most extant resilience studies have not explicitly considered the geographic accessibility prerequisite inherent to tourism and hospitality products. In this study, leveraging the social contagion theory, we present a holistic research framework to investigate the influence of geographic and social proximities, two pivotal social contagion mechanisms, on business resilience. We also delve into moderating factors to discern the conditions under which contagion effects are amplified or attenuated. To validate our theoretical model, we select the restaurant industry as our research context, given its severe impact from COVID-19. Utilizing an extensive dataset from Yelp, encompassing ten U.S. cities varying in sizes and geolocations, our findings indicate that both geographic and social influences exert significant direct effects on resilience. Additionally, these effects exhibit considerable variations contingent upon product attributes, customer characteristics, and geographic factors. Theoretically, we are the first to substantiate the role of social contagion theory in examining resilience, enriching our understanding of the social network mechanism of behavioral contagion among customers during the COVID-19 pandemic. We also offer valuable practical implications for various stakeholders in supporting their management strategies and decision-making in developing effective plans and preparations, minimizing adverse impacts, and ensuring sustainability in the face of future disruptions.

Do global innovation networks influence the status of global value chains? Based on a patent cooperation network perspective

Under the impact of economic globalization, the internationalization of production and trade has given rise to global value chains (GVCs) and global innovation networks (GINs); however, few studies have explored the link between these two networks. This paper constructs a global innovation cooperation network based on patent cooperation data and then analyzes the impact of different network characteristics on improving global value chain status. Empirical research shows that the characteristics of GINs significantly affect GVC status; the higher the eigenvector centrality and network connectivity, the higher the country’s GVC status. Moreover, a higher degree of constraint is associated with a lower GVC status for the country concerned. The heterogeneity analysis further reveals apparent differences in the impact of GINs on different countries, manifesting as a catalytic effect in developed countries and as a disincentive in emerging economies; the effect is less apparent in developing countries. This paper provides theoretical support for the development of technological innovation activities and provides a reference for government departments to formulate policies on cross-border technological innovation cooperation.

Investigating urban mobility through multi-source public transportation data: A multiplex network perspective

The integration of multi-source and diverse spatio-temporal travel data provides a comprehensive insight into urban mobility. Using data from Shenzhen's public transportation system, this study presents an analytical framework based on multiplex networks to examine variations in multi-mode public transportation usage (metro, bus, taxi, and shared bike) and their correlation with the built environment. This framework encompasses the analysis of network topological characteristics, centrality, and communities. The examination of network topological characteristics reveals that the multiplex transportation network exhibits high global accessibility and local connectivity. Network centrality analysis, focusing on weighted outdegree centrality, captures the patterns of public transportation ridership. Centrality modeling, employing the light gradient boosting machine, demonstrates a nonlinear relationship between ridership and the built environment. Factors including population density, residential land use percentage, entertainment service density, restaurant density, and metro station density consistently exhibit positive correlations with ridership across different times of the day. The community structure analysis, using consensus community detection, indicates that distinct urban areas exhibit clustering behavior based on public transportation demand patterns, forming distinct communities that closely align with the functional zoning of urban planning. These findings could provide valuable insights for the strategic planning of transportation services and the built environment.

A network perspective on posttraumatic stress disorder and comorbid borderline personality disorder symptoms

ABSTRACT Background: Comorbidity between posttraumatic stress disorder (PTSD) and borderline personality disorder (BPD) is surrounded by diagnostic controversy and although various effective treatments exist, dropout and nonresponse are high. Objective: By estimating the network structure of comorbid PTSD and BPD symptoms, the current study illustrates how the network perspective offers tools to tackle these challenges. Method: The sample comprised of 154 patients with a PTSD diagnosis and BPD symptoms, assessed by clinician-administered interviews. A regularised partial correlation network was estimated using the GLASSO algorithm in R. Central symptoms and bridge symptoms were identified. The reliability and accuracy of network parameters were determined through bootstrapping analyses. Results: PTSD and BPD symptoms largely clustered into separate communities. Intrusive memories, physiological cue reactivity and loss of interest were the most central symptoms, whereas amnesia and suicidal behaviour were least central. Conclusions: Present findings suggest that PTSD and BPD are two distinct, albeit weakly connected disorders. Treatment of the most central symptoms could lead to an overall deactivation of the network, while isolated symptoms would need more specific attention during therapy. Further experimental, longitudinal research is needed to confirm these hypotheses. Trial registration: ClinicalTrials.gov identifier: NCT03833453.

The higher-order moments connectedness between rare earth and clean energy markets and the role of geopolitical risk:New insights from a TVP-VAR framework

Against the backdrop of escalating geopolitical risks, the interdependence between rare earth and clean energy markets are crucial for cross-market investors and regulators. However, previous research has not adequately examined the higher-order moment connections between these markets. This study addresses this gap by integrating GRACHSK and TVP-VAR methods to explore the asymmetric and tail risk connectedness between rare earth and solar, wind, nuclear, and electric vehicle markets. Further, we investigated how geopolitical risks impact these connections over time. The results reveal significant higher-order moment connectedness between the clean energy and rare earth markets, each serving as an exporter and receiver, respectively. Among these, wind energy and solar energy exhibit significant net spillovers of skewness and kurtosis to rare earths. These high-order moment spillovers are time-varying and exacerbate during major geopolitical crises, such as the COVID-19 pandemic and the Russian-Ukraine war. The portfolios with minimum connectedness are effective at hedging extreme risks, particularly in terms of kurtosis. It implies that portfolios constructed based on higher-order moments can better hedge against extreme risks. GPR exacerbates higher-order moment spillovers between rare earth and clean energy markets in the short- and medium-term. In particular, the impulse response of kurtosis spillovers to GPR shocks shows prominent and frequent fluctuations, indicating that extreme risk spillover effects with significant lag effects would rapidly diffuse across various markets when geopolitical risks occur. The research findings contribute to three aspects: First, the connectedness analysis offers a network and dynamic perspective on extreme risk management between rare earth and clean energy markets. Second, higher-order moments can enhance the returns of diversified cross-market investment portfolios. Third, the conclusions aid in understanding the evolving patterns of rare earth and clean energy markets under geopolitical risk events, facilitating timely investment and regulatory strategies.

ESG peer effects and corporate financial distress: An executive social network perspective

AbstractBased on the data of listed firms in China from 2009 to 2020, this study investigates whether environmental, society and governance (ESG) peer effects reduce the risk of corporate financial distress from an executive social network perspective. Using two‐stage least squares method, our empirical results suggest that the ESG peer effects exist in executive social networks, and the ESG peer effects can alleviate corporate financial distress. ESG subcategory analysis shows that the governance peer effect has the most obvious alleviating effect on financial distress. The negative impact of ESG peer effects on corporate financial distress is stronger when firms have high network power, network cohesion and network control in executive social networks. Our conclusions still hold after a series of robustness tests. Our research expands the literature on peer effects from the perspective of social relations, and sheds additional light on the critical role of ESG peer effects in financial risk management.

The relationship between clustering and networked Turing patterns.

Networked Turing patterns often manifest as groups of nodes distributed on either side of the homogeneous equilibrium, exhibiting high and low density. These pattern formations are significantly influenced by network topological characteristics, such as the average degree. However, the impact of clustering on them remains inadequately understood. Here, we investigate the relationship between clustering and networked Turing patterns using classical prey-predator models. Our findings reveal that when nodes of high and low density are completely distributed on both sides of the homogeneous equilibrium, there is a linear decay in Turing patterns as global clustering coefficients increase, given a fixed node size and average degree; otherwise, this linear decay may not always hold due to the presence of high-density nodes considered as low-density nodes. This discovery provides a qualitative assessment of how clustering coefficients impact the formation of Turing patterns and may contribute to understanding why using refuges in ecosystems could enhance the stability of prey-predator systems. The results link network topological structures with the stability of prey-predator systems, offering new insights into predicting and controlling pattern formations in real-world systems from a network perspective.

Advanced Neurocircuit Mapping via Non-invasive Magnetic Resonance Imaging Techniques

The brain comprises a complex network of anatomically distinct regions (each with specialized functions) that collaborate to support various cognitive processes. Therefore, it is important to understand the brain from the perspective of a complex network. Functional magnetic resonance imaging (fMRI) is increasingly being accepted for its ability to provide useful insights into brain function. Among the fMRI techniques available in clinical practice, resting-state fMRI (rsfMRI) represents the core method for mapping brain activity in the absence of specific tasks; studies have reported the usefulness of rsfMRI in the investigation of various human diseases. Functional brain networks, which consist of interconnected regions that show correlated activities, are typically depicted as functional connectivity (FC). FC analysis using rsfMRI data provides extensive information, revealing intrinsic resting-state networks and highlights deviations in network structure among patients with psychiatric disorders. Such network insights not only deepen our understanding of the brain but also facilitate assessment of network alterations associated with psychiatric and neurodegenerative diseases.

Meta-Path Semantic and Global-Local Representation Learning Enhanced Graph Convolutional Model for Disease-Related miRNA Prediction.

Dysregulation of miRNAs is closely related to the progression of various diseases, so identifying disease-related miRNAs is crucial. Most recently proposed methods are based on graph reasoning, while they did not completely exploit the topological structure composed of the higher-order neighbor nodes and the global and local features of miRNA and disease nodes. We proposed a prediction method, MDAP, to learn semantic features of miRNA and disease nodes based on various meta-paths, as well as node features from the entire heterogeneous network perspective, and node pair attributes. Firstly, for both the miRNA and disease nodes, node category-wise meta-paths were constructed to integrate the similarity and association connection relationships. Each target node has its specific neighbor nodes for each meta-path, and the neighbors of longer meta-paths constitute its higher-order neighbor topological structure. Secondly, we constructed a meta-path specific graph convolutional network module to integrate the features of higher-order neighbors and their topology, and then learned the semantic representations of nodes. Thirdly, for the entire miRNA-disease heterogeneous network, a global-aware graph convolutional autoencoder was built to learn the network-view feature representations of nodes. We also designed semantic-level and representation-level attentions to obtain informative semantic features and node representations. Finally, the strategy based on the parallel convolutional-deconvolutional neural networks were designed to enhance the local feature learning for a pair of miRNA and disease nodes. The experiment results showed that MDAP outperformed other state-of-the-art methods, and the ablation experiments demonstrated the effectiveness of MDAP's major innovations. MDAP's ability in discovering potential disease-related miRNAs was further analyzed by the case studies over three diseases.

Identification of Key Risk Nodes and Invulnerability Analysis of Construction Supply Chain Networks

The construction supply chain confronts interruption risks that raise significant concerns regarding industry safety and stability. Consequently, exploring risk management strategies from both enterprise and supply chain network perspectives is crucial. This study employs complex network theory and the cascade failure model to propose a methodology tailored to the unique characteristics of the construction supply chain, facilitating the identification of key risk nodes and the conduct of invulnerability analyses. By evaluating the importance of construction enterprise nodes and their risk propagation ability during cascade failures, this method enables the comprehensive identification of key risk node enterprises within the construction supply chain network. Furthermore, this study examines and discusses strategies for enhancing network invulnerability by taking into account node capacity, load, and resilience. Empirical results indicate that the key nodes and risk nodes in the construction supply chain network are mainly located upstream and downstream, displaying specific distribution patterns. In addition to core enterprises, key risk nodes comprise some strong suppliers at the intermediary and lower tiers of the supply chain. Adjustments to node enterprise parameters like capacity, load, and resilience have diverse impacts on the invulnerability of the construction supply chain network. This study clarifies the distribution patterns of key risk nodes within the construction supply chain network and the variations in network invulnerability under particular conditions, providing valuable insights for risk management decision-making.

Psikopatoloji Araştırmalarında Yeni Bir Yöntem: Ağ Analizi

Though network analysis has a long history in both natural and social sciences it has emerged as a new method in psychology in recent years. Unlike medical disorders, mental disorders are not observable in laboratory. However, we can identify them by the way of observable symptoms. According to the network perspective, a disorder occurs when an external event triggers a psychological symptom. Activated symptom also interacts with other symptoms and forms a pattern of symptoms. Network approach criticizes traditional categorical diagnostic approach and focuses on symptom organization. Probably, treating the most effective symptom will accelerate recovery process and provide more effective treatment. Network analysis can be used in both cross-sectional and longitudinal studies. Psychological networks provide opportunities to investigate direction of the relationship among symptoms, comorbidity, external triggers of psychological symptoms, effectiveness of treatment, comparison of symptom pattern according to sample characteristics. Despite the utility of psychological networks, accuracy of them has been questioned and certain methods to prove accuracy of networks proposed as response. Technological progress in recent years enabled network analysis to be more eligible in psychology. R Statistics software is very useful in network analysis which is totally free and open sourced and supported by many additional packages. This review article aims is to provide information about usage of network analysis in psychology, especially in clinical research. In the first part historical and theoretical background of network analysis was introduced and in the following parts structure, validity of psychological networks and R Statistics Software which is used for conducting network analysis were explained briefly.

Modelling the impact of introducing first-generation narrowbody hydrogen aircraft on the passenger air transportation network in Europe

The global aviation industry faces a pronounced challenge to mitigate its contribution to anthropogenic climate change, which is caused by burning fossil fuels. One potential pathway is the transition towards using green hydrogen as zero-carbon fuel. Next to technological, infrastructural, and economical challenges, this would introduce a wide range of operational and infrastructural changes to a system centred around safety and reliability. This study employs a holistic approach and examines these changes from a flight network perspective, focusing on passenger transport in Europe. It utilizes network modelling and linear optimization techniques to determine network changes imposed by the introduction of first-generation narrowbody hydrogen aircraft. The findings are threefold: First, the study shows that the air transportation network can be adequately re-created using demand and direct operating cost optimization only, without considering revenue and airline-specific factors. Second, the introduction of hydrogen aircraft will have substantial impact on network structure, incl. flight frequency, capacity, and stopover routes, depending on seat capacity and range of the hydrogen aircraft design. Lastly, it also shows that airports that have a favourable green hydrogen supply will benefit from the introduction of hydrogen aviation.