Network Perspective Research Articles

The application of the network approach to quality of life and blood markers in Brazil

Although it is generally considered that quality of life (QoL) is related to blood markers, there are different approaches to the relation between these variables. We proposed here a dynamic interaction test. The aim was to ascertain which blood indicators relate to QoL through a network perspective in a common population. Answered a questionnaire and conducted blood exams 108 Brazilians. Of these, 84 were female, with a mean age of 43.85 years old (SD = 16.20). Data analysis indicated that CRP, T4 (FT4), Thyroid-stimulating hormone, and Cortisol bridges with QoL dimensions. The data suggest that QoL is related to certain blood markers, but some trends were less than the literature has indicated. These relations are complex, and our findings suggest that the dynamic network structure expands the range of QoL and blood markers researches making it possible to investigate a new range of questions and analyses.

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.

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.

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

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.

The evolution of transnational municipal knowledge networks

AbstractThe recent growth of transnational municipal networks requires a better understanding of their evolution. For some actors, this growth has resulted in arbitrary or top‐down decisions on network membership followed by low commitment and inefficient use of time and financial resources. By reinterpreting secondary data through an evolutionary approach, this paper argues that the overall systemic nature of the networks is shaped not only by their institutional settings and the composition of actors but also by a set of specific qualities (coherence, boundedness and unified function) that may change over time. Based on the network perspective and system approach, the paper advances our understanding of transnational knowledge networks and the maximising of their effectiveness. Using exemplars, it suggests a conceptual framework for the evolution of transnational municipal knowledge networks.

Adaptive Strategies and Sustainable Innovations of Chinese Contractors in the Belt and Road Initiative: A Social Network and Supply Chain Integration Perspective

As global economic integration and rapid technological advancements transform international business, international engineering contracting has become essential for achieving sustainable development goals (SDGs). This paper investigates the significant impact of China’s strategic initiatives, notably the “Going Global” strategy and the Belt and Road Initiative (BRI), on the operational practices of Chinese enterprises involved in overseas investments. Central to this transformation is the Engineering, Procurement, and Construction (EPC) model, which emphasizes the integration of supply chain management and stakeholder collaboration to enhance performance in international EPC projects and underscores the crucial role of these elements in promoting sustainability. Incorporating insights from social network data analysis, this study reveals that contractors collaborating with various stakeholders—such as owners/consulting engineers, domestic and foreign customs departments, and group headquarters/design parties—exhibit a high degree of similarity in personnel profiles. This suggests that the internal organizational structure and personnel allocation of contractors could be optimized to enhance operational efficiency, aligning with the collaborative patterns identified. This study addresses a critical research gap by exploring how effective supply chain management and collaborative stakeholder engagement within multinational EPC projects contribute to sustainable outcomes. Employing advanced social network analysis software, the research examines the complex interactions among stakeholders and their influence on procurement dynamics. Findings indicate that strong relational networks and strategic collaborations significantly enhance procurement efficiency and project success, underscoring the importance of supply chain integration. Ultimately, integrating supply chain management principles into the EPC model not only offers innovative perspectives for advancing sustainability in international projects but also provides actionable insights for improving project outcomes within the BRI framework. This research underscores the pivotal role of supply chain organization and stakeholder cooperation in achieving sustainability objectives, thereby enriching the discourse on sustainable enterprise operation and supply chain management in the context of global initiatives.

Relay Assisted Device-to-Device Communication for Future Generation Wireless Networks: A Review from Architectural and Challenges Perspective

The rapid evolution of wireless communication technologies, along with the increasing demand for efficient and reliable data transfer, has driven the rethinking of traditional cellular network architectures in the context of next-generation networks. In conventional cellular systems, communication between users typically occurs through base stations. However, to improve efficiency, scalability, and spectral utilization, device-to-device (D2D) communication has been introduced, enabling direct data transmission between users without base station involvement. In this paper, we present a comprehensive review of D2D communication from architectural and challenges perspective for future generation networks. Additionally, the study focuses on relay-assisted D2D (RAD2D) communication, examining the role of machine learning (ML) and artificial intelligence (AI) in optimizing relay selection processes. In light of the existing literature, challenges for implementing RAD2D are discussed from different perspectives such as relay selection, energy efficiency, secure communication, resource allocation, and the management of dynamic network conditions

The criminal activities and operational roles of Australian neo-jihadists: a network perspective

ABSTRACT Despite the significant global repercussions of terrorism, including loss of lives and economic resources, research on its relationship with crime has been limited to group-level analyses. This study examines the individual criminal activities of Australian Neo-Jihadists, via social network analysis from an individual perspective. Judicial documents and newspaper articles are analysed to determine the criminal activities and operational roles of network actors. Findings suggest a prominence of criminal activities, and a significant relationship between market criminal activities and facilitation of terrorist operations. The study concludes with suggestions for future research, emphasising the potential for disrupting terrorist networks by focusing on the new crime-terror nexus.

Connector hubs in semantic network contribute to creative thinking.

Semantic memory offers a rich repository of raw materials (e.g., various concepts and connections between concepts) for creative thinking, represented as a semantic network. Similar to other networks, the semantic network exhibits a modular structure characterized by modules with dense internal connections and sparse connections between them. This organizational principle facilitates the routine storage and retrieval of information but may impede creativity. The present study investigated the effect of hub concepts with varying connection patterns on creative thinking from the perspective of a modular structured semantic network. By analyzing a large-scale semantic network, connector hubs (C-hubs) and provincial hubs (P-hubs) were identified based on their intra- and intermodule connections. These hubs were used as cue words in the alternative uses task, a widely used measure of creative thinking. Across four experiments, behavioral and neural evidence indicated that C-hubs facilitate the generation of more novel and remote ideas compared to P-hubs. However, this effect is predominantly observed in the early stage of the creative thinking process, involving changes in brain activation and functional connectivity in core regions of the default mode network and the frontoparietal network, including the dorsolateral prefrontal cortex, angular gyrus, and precuneus. Neural findings suggest that the superior performance of C-hubs relies on stronger interactions between automatic spreading activation, controlled semantic retrieval, and attentional regulation of salient information. These results provide insight into how concepts with varying semantic connection patterns facilitate and constrain different stages of the creative thinking process through the modular structure of semantic network. (PsycInfo Database Record (c) 2024 APA, all rights reserved).