Network Perspective Research Articles

Evaluating manufacturing plants readiness for distributed ledger technology: a network framework for ambiguity levels

The utilisation of blockchain technology has gained significant traction within contemporary supply chains owing to its ability to enhance transparency, security, and traceability. Manufacturing plants, as pivotal components of the supply chain, stand to benefit from improved tracking and transparency of goods movement, real-time visibility, quality control processes, and adherence to industry standards through blockchain implementation. Nonetheless, without a comprehensive assessment of manufacturing plants’ readiness to embrace blockchain technology, the anticipated benefits may give way to unforeseen challenges. In this study, a novel network framework is offered to evaluate manufacturing plants’ readiness for adopting distributed ledger technology, specifically blockchain, under varying levels of ambiguity, including high (fuzzy) and low (scenario) ambiguity. This framework is distinguished by its ability to address uncertainty in evaluations, incorporating both scenario-based and fuzzy programming approaches. Furthermore, the framework treats evaluation criteria as interconnected entities, fostering a network perspective rather than a black-box approach. The proposed framework is then validated through a case study involving five manufacturing plants and twenty-four evaluation criteria. Our findings underscore the pivotal role of uncertainty considerations in ranking manufacturing plants, with the fifth plant emerging as the frontrunner across both fuzzy and scenario-based assessments in most instances.

The Central Role of Daily Hopeful Feelings in Predicting Well-Being: A Network Perspective.

Hope is associated with many positive outcomes. However, most research on hope has focused on its cognitive aspect, with few studies examining the affective aspect. To further clarify the nature of hope and its association with well-being, this study examined the relationship between the affective aspect of hope-namely, hopeful feelings-and various aspects of well-being. A daily diary analysis was conducted with 187 Chinese adults (meanage = 28.17 ± 5.56). Hopeful feelings and affective (positive and negative emotions), eudaimonic (meaning in life), and evaluative (life satisfaction) aspects of well-being were assessed twice per day (morning and night) for 14 consecutive days. A multilevel vector autoregressive model was used to investigate the temporal, contemporaneous, and between-person networks of hopeful feelings with positive emotion and well-being. Hopeful feelings had the strongest outstrength in the temporal networks and were the most central node in the between-person networks. In addition, hopeful feelings exhibited significant cross-lagged predictive roles on all the well-being nodes except negative emotion. These findings revealed a close association between hopeful feelings and well-being, with hopeful feelings predicting various aspects of daily well-being. Future interventions to improve well-being should focus on the enrichment of daily hopeful feelings.

The impact of multiplex relationships on households’ informal farmland transfer in rural China: A network perspective

Over the last three decades, farmland transfers in rural China have significantly increased. Despite this rise, informal farmland transfers within social networks continue to be common in many villages, underscoring the need for a more nuanced understanding of their determinants. Existing literature extensively explores the influence of social networks on farmland transfers, it often overlooks the distinction between formal and informal transactions and primarily focuses on binary relationships. Limited data availability and constraints in econometric methodology have impeded quantitative estimation of the impact of farmers’ interactions on informal farmland transfers at the local levels. To address this issue, we developed a framework that integrates embeddedness theory and social interaction theory to uncover the mechanism between multiplex relationship and informal farmland transfers from a network perspective. Using survey data collected from all 341 households in a village in central China, we constructed both relationship networks and informal farmland transfer network among peasant households. We then employed the logistic regression quadratic assignment procedure (LRQAP) to quantify the impact of multiplex relationships and network structures on informal farmland transfers. The results reveal a significant positive correlation between multiplex relationships and informal farmland transfer behaviors among peasant households. Specifically, households are more likely to engage in informal farmland transfer through relationships characterized by emotional connections and reciprocal exchanges, rather than through relationships based on cooperative support. Furthermore, our findings indicate that multiplex relationships influence informal farmland transfer behaviors through network structure effects, such as reciprocity and preferential attachment, rather than through transitivity. These results highlight the importance of leveraging multiplex relationships to facilitate and regulate informal farmland transfers amid rural decline and land use change.

Development of a deep learning–based feature stream network for forecasting riverine harmful algal blooms from a network perspective

Global increases in the occurrence of harmful algal blooms (HABs) are of major concern in water quality and resource management. A predictive model capable of quantifying the spatiotemporal associations between HABs and their influencing factors is required for effective preventive management. In this study, a feature stream network (FSN) model is proposed to provide daily forecasts of cyanobacteria abundance at multiple monitoring sites simultaneously in a river network. The spatial connectivity between monitoring sites was expressed as a directed acyclic graph comprising edges and nodes representing flows and monitoring sites, respectively. Furthermore, a segment-wise node connection structure was developed to extract the latent features of a river segment comprising individual nodes and sequentially transfer them to the downstream segment(s). In addition, a feature engineering–attention hybrid mechanism was employed to address temporal mismatches among different monitoring schemes while adding explainability to the model. Consequently, the FSN showed improved predictive performance, temporal resolution, and explainability for multi-site forecasts of HAB in a single model framework. The developed model was applied to a bloom-prone middle course of the Nakdong River, South Korea. Various hydrological, environmental, and biological factors were utilized for forecasting the cyanobacteria abundance. The FSN exhibited a high degree of accuracy across the sites for the test data with a coefficient of determination in the range of 0.64–0.71 and root mean square error in the range of 2.06–2.26 cells/mL on natural log scales. Although the relative importance of input features varied across the sites, the features extracted from nearby nodes consistently exhibited high importance in forecasting the cyanobacteria abundance. These explanations indicate that the proposed model can successfully characterize the spatial hierarchy of a river network. A scenario analysis suggested that reduced total nitrogen loads in the effluents from the wastewater treatment plant and the combined operations of upstream and downstream weirs were effective in managing HABs.

Robustness Analysis of High-Speed Railway Networks Against Cascading Failures: From a Multi-Layer Network Perspective

Robustness Analysis of High-Speed Railway Networks Against Cascading Failures: From a Multi-Layer Network Perspective

A Networking Perspective of Volumetric Video Service: Architecture, Opportunities, and Case Study

A Networking Perspective of Volumetric Video Service: Architecture, Opportunities, and Case Study

Research on Investor Sentiment from the Perspective of a Complex Network

Research on Investor Sentiment from the Perspective of a Complex Network

Resilience assessment of the aircraft manufacturing core products supply chain: the international trade network perspective

Resilience assessment of the aircraft manufacturing core products supply chain: the international trade network perspective

Understanding trends, patterns, and dynamics in global company acquisitions: a network perspective

Studying acquisitions offers invaluable insights into startup trends, aiding informed investment decisions for businesses. However, the scarcity of studies in this domain prompts our focus on shedding light in this area. Employing Crunchbase data, our study delves into the global network of company acquisitions using diverse network analysis techniques. Our findings unveil an acquisition network characterized by a primarily sparse structure comprising localized dense connections. We reveal a prevalent tendency among organizations to acquire companies within their own country and industry, as well as those within the same age bracket. Furthermore, we show that the country, region, city, and category of the companies can affect the formation of acquisition relationships between them. Our temporal analysis indicates a growth in the number of weakly connected components of the network over time, accompanied by a trend toward a sparser network. Through centrality metrics computation in the cross-city acquisition network, we identify New York, London, and San Francisco as pivotal and central hubs in the global economic landscape. Finally, we show that the United States, United Kingdom, and Germany are predominant countries in international acquisitions. The insights from our research assist policymakers in crafting better regulations to foster global economic growth, and aid businesses in deciding which startups to acquire and which markets to target for expansion.

Analyzing disruptions in post-pandemic population return patterns: A network perspective on Chinese cities

The COVID-19 pandemic has fundamentally reshaped global socio-economic structures, precipitating a profound transformation in people's lifestyles. An in-depth analysis of the disruptions in post-pandemic population returns patterns and the evolving driving factors can facilitate socio-economic recovery, development, and macro-control. This paper employs social network analysis to examine the spatial patterns and evolving urban locational attributes of the Population Mobility Network (PMN) during post-holiday returns in major Chinese cities amid the pandemic, assessing the impact of varying proximities on the PMN. The results indicate that: (1) During the pandemic, the geographic patterns of population mobility in China underwent significant fluctuations, with distinct regional and temporal variations, while intercity connections increasingly shifted toward shorter trips. (2) From 2019 to 2022, major Chinese cities experienced notable changes in status and connectivity, evolving into a multi-centric structure within the national population mobility network. (3) Community clusters predominantly adhere to provincial demarcations, maintaining stable structures within major urban agglomerations despite intense intra-regional competition, with clear distinctions in community stability between northern and southern regions. (4) Beyond traditional socio-economic factors, the level of digital finance has emerged as a new driver of population mobility, with the varied attributes of inter-city relationships also significantly influencing the PMN. Studying spatial patterns and urban location from the perspective of population mobility can inform post-pandemic optimization of national resource allocation and urban recovery strategies.

Spatial Association Network Structure and Influencing Factors of Carbon Emission Efficiency in China’s Logistics Sector

Based on relational data and a network perspective, this study calculates the carbon emission efficiency of China's logistics industry from 2006 to 2021 and analyzes the characteristics of the spatial correlation network structure of this efficiency through social network analysis. The results are as follows: (1) During the study period, the carbon emission efficiency of China's logistics industry exhibited a fluctuating downward trend, with notable regional disparities. Overall, there was an imbalance among regions, with the eastern region showing higher efficiency than the central region, and the central region higher than the western region. (2) From the perspective of the overall network structure, the spatial correlation of carbon emission efficiency in China's logistics industry tended to become closer, with a relatively stable and balanced spatial correlation structure. Additionally, (3) differences in spatial distance, economic development level, energy intensity, urbanization level, and industrial structure had significant impacts on the formation of the spatial correlation network of carbon emission efficiency in China's logistics industry.

Assessing the dimensions and structure of thwarted interpersonal needs

IntroductionThe Interpersonal Needs Questionnaire-15 (INQ-15) assesses interpersonal needs comprising suicidal desire based on the Interpersonal Theory of Suicide (IPTS): Thwarted belongingness (TB) and perceived burdensomeness (PB). The IPTS is applied to many psychological-affective states and disorders, collectively showing state-specific pathways to suicidal features. Given the prevalent application of the IPTS, determining how the constructs are comprised and operate is critical. Consistent with the network perspective of psychological constructs, we used network and algorithmic methods to evaluate thwarted interpersonal needs. MethodsA sample (N = 863) aggregated from two independent studies (Sokol & Serper, 2019; Sokol et al., 2023) was used for analysis. ResultsNetwork analysis identified three dimensions that were invariant by suicide-risk status: PB, and two dimensions from the TB items. Accounting for wording effects, one-dimensional structures presented in 9 %–23 % of replicates. The loadings comparison test algorithm suggested that the constructs were consistent with a network composition (over latent), and centrality indices identified meaningful strong and bridge features. LimitationsThe samples were demographically homogeneous, and more diverse samples in terms of sexuality, racial/ethnic identification, culture, and socioeconomic status might yield different findings. ConclusionFindings indicate that thwarted interpersonal needs are comprised of perceived burdensomeness, loneliness, and absence of reciprocal care dimensions and that loneliness-related experiences may be mechanisms by which PB and TB-related experiences co-occur. Additionally, results showed that the INQ-15 may have previously unquantified method effects, but the overall identified dimensions are stable. These results yield a nuanced conceptualization of interpersonal needs and their function.

Discrete‐Time Optimal Control of State‐Constrained Nonlinear Systems Using Approximate Dynamic Programming

ABSTRACTThis article investigates the optimal control problem (OCP) for a class of discrete‐time nonlinear systems with state constraints. First, to overcome the challenge caused by the constraints, the original constrained OCP is transformed into an unconstrained OCP by utilizing the system transformation technique. Second, a new cost function is designed to alleviate the effect of system transformation on the optimality of the original system. Further, a novel off‐policy deterministic approximate dynamic programming (ADP) scheme is developed to obtain a near‐optimal solution for the transformed OCP. Compared to existing off‐policy deterministic ADP schemes, the developed scheme relaxes the requirement on the learning data and saves computing resources from the perspective of training neural networks. Third, considering approximation errors, we analyze the convergence and stability of the developed ADP scheme. Finally, the developed ADP with the designed cost function is tested in two numerical cases, and simulation results confirm its effectiveness.

Research on evaluation of knowledge interaction indicators in multi-team systems based on management entropy

Efficient knowledge interaction among teams can enhance the collaborative performance of Multi-team Systems (MTSs). Identifying and addressing issues within MTS knowledge interaction is a current topic in knowledge-driven MTS collaboration. However, there is a lack of scientific and measurable evaluation indicators and methods for effectively evaluating MTS knowledge interactions to aid decision-making. This study, integrating a network perspective with complex systems thinking and the theoretical principles of management entropy, constructs a systematic evaluation system for MTS knowledge and a corresponding methodological framework. Validated and analyzed through two case studies in diverse contexts, this system and method enable managers to pinpoint crucial indicators affecting the effectiveness of MTS knowledge at various stages, especially critical junctures that may arise during the evolving development of the MTS. This provides a basis for decision-making to enable timely managerial interventions at crucial moments.

A new way to conceptualize intolerance of uncertainty among adolescents: Embracing the network perspective.

Intolerance of uncertainty (IU), a pivotal transdiagnostic risk factor in psychopathology, is defined as a dispositional incapacity to withstand uncertainty distress, driving maladaptive cognitive, emotional and behavioural reactions to uncertainty. However, the intricate interplay among these components, particularly in adolescents, remains underexplored; yet understanding this interplay is crucial for supporting mental health. To address this gap, we employed a network approach to conceptualize IU in 5672 non-clinical Chinese adolescents (Mage = 14.13 years, SDage = 1.96 years, range = 10-19 years, 46.6% boys), combining graphical Gaussian models (GGM) and directed acyclic graphs (DAG). Our analyses revealed a tripartite network comprising cognitive, behavioural and emotional components. Notably, 'frustration' and 'work with hindrance' emerged as key drivers, while 'catastrophizing belief' served as a critical bridge linking different components. These findings underscore the importance of alleviating uncertainty-induced frustration and enhancing coping skills for behavioural impediments to mitigate adolescent IU. Additionally, therapeutic interventions should prioritize modifying and re-evaluating catastrophizing beliefs related to uncertainty.

Understanding scientific knowledge evolution patterns based on egocentric network perspective

Understanding scientific knowledge evolution patterns based on egocentric network perspective

Co-jumps in the Chinese stock market before, during and after the COVID-19 pandemic: A network perspective

This paper investigates co-jumps in the Chinese stock market before, during and after the COVID-19 pandemic from a network perspective. The higher co-jump intensity and the tighter co-jump network connections during the pandemic suggest increased volatility and crash risk. Besides, the similarity of closeness centrality between the networks during and after the pandemic indicates a persistent impact of the pandemic on the Chinese stock market. Moreover, the community detection results show that the pandemic refines and distinguishes the network’s community structure. Furthermore, during the pandemic, the correlation between community structure and industry classification is stronger compared to non-pandemic periods.

Robustness analysis of smart manufacturing systems against resource failures: A two-layered network perspective

Complex and changing environments often cause resource failures in smart manufacturing systems (SMSs), significantly affecting their robustness. This paper introduces a novel methodology to assess the robustness of SMSs facing resource failures, using a complex network approach. It divides SMSs into social and technical layers, analyzes resources and relationships within and between these layers, and establishes a two-layered network model. It also categorizes various types of failures and proposes three robustness metrics to evaluate system performance at individual, local, and global levels. Simulations visually demonstrate the methodology and key findings: (1) the robust-but-fragile trait of SMSs only reacts to node failures and keeps significant in terms of the gradient of robustness; (2) there exists no edge failure that keeps damaging system robustness to the maximal or minimal degrees, and edge failures cause less damage to system robustness than node failures; (3) when failures occur, SMS robustness at all levels changes with inconsistent paces, and the optimal link mode varies by network structures and failure strategies. Finally, managerial implications are presented to guide practical robustness control at different stages of SMS lifecycles.

Partners in Force? Understanding Police Use of Force from a Network Perspective

Partners in Force? Understanding Police Use of Force from a Network Perspective

Connections and Spatial Network Structure of the Tourism Economy in Beijing–Tianjin–Hebei: A Social Network Perspective

In the context of the coordinated development of Beijing–Tianjin–Hebei, examining the spatial connections and network structure characteristics of the tourism economy holds substantial theoretical and practical significance. This study constructs a tourism economic quality evaluation index system from four dimensions and optimizes the tourism economic gravity model by incorporating time distance and weight factors to dynamically simulate the intensity of tourism economic spatial connection (ITESC). Based on the new perspective of relational data, social network analysis is employed to investigate the spatial correlation network characteristics and group structure changes in the tourism economy within the Beijing–Tianjin–Hebei urban agglomeration (BTHUA). The results demonstrate a significant improvement in overall tourism economic quality from 2013 to 2022, with the average quality increasing from 0.128 in 2013 to 0.157 in 2022. Tourism economic spatial connections are prevalent among cities, exhibiting heterogeneous characteristics, characterized by density in the north and relatively sparse in the south. The network cohesion of ITESC strengthens, and the network structure evolves from a highly centralized to a multi-centralized configuration, with increasingly frequent cooperation and interaction in tourism between cities. Beijing, Tianjin, and Shijiazhuang occupy central positions in the network, exhibiting strong ITESC and a high degree of connectivity, generating a significant “trickle-down effect” on peripheral cities. Consequently, strengthening ITESC between peripheral cities and core areas, as well as enhancing the bidirectional spillover of tourism development between cities, is crucial for the future sustainable and coordinated development of tourism in the BTHUA.