Network Perspective Research Articles

Network analysis of frailty indicators in hospitalized elderly patients: unveiling the role of depression and hemoglobin as core factors.

Frailty is a significant concern among hospitalized older adults, influenced by multiple factors. Understanding the complex interactions between these variables can be facilitated through a network perspective. This study aimed to identify the core factor and physiological indicator of frailty in hospitalized elderly patients and visualize their interactions within the network structure. Frailty was assessed using the Tilburg Frailty Indicators, with a score of 5 or higher indicating frailty. Additional variables related to sociodemographic, physical and clinical, psychological and cognitive aspects, as well as physiological indicators, were extracted from electronic health records. A partial correlation network analysis was conducted using an adaptive LASSO algorithm, based on univariate correlation and logistic regression, to examine the network structure and identify influential nodes. The average age of participants was 70.74 ± 7.52years, with 24.27% classified as frail. Frailty was associated with 38 of 145 initially included variables (P < 0.05). The network analysis revealed depression as the most central node, followed by drugs used, sleep disorders, loneliness, masticatory obstacles, drinking, and number of teeth missing. Hemoglobin emerged as the most central biochemical indicator in the network, based on network center index analysis (Strength = 4.858, Betweenness = 223, Closeness = 0.034). Frailty in hospitalized older adults is influenced by various social, physical, and psychological factors, with depression as the core factor of utmost importance. Changes in hemoglobin levels could serve as an essential indicator. This innovative network approach provides insights into the multidimensional structure and relationships in real-world settings.

When young people age out of care: Foster care in a life course and network perspective

AbstractResearch highlights the importance of supportive relations for young people leaving care. Foster carers give an important contribution to such support. However, there is less knowledge about foster carers' views about the relational contact after the young person has aged out of care. This article explores foster carers' perspectives building on interviews with foster carers from both Norway and Sweden about their views on relational continuity. Life course and network theory are used as theoretical lenses to understand the opportunities and challenges for further contact both with cares and extended foster family. Findings suggest that foster carers have a lifelong perspective, still the relationship can be uncertain and some result in breaks. Viewed from a life course perspective, the relationships can also be renewed, and foster carers and other members of the foster family seem to be important sources of support for young people with a care background.

APPLICATIONS OF SOCIAL NETWORK ANALYSIS IN PROMOTING CIRCULAR ECONOMY: A LITERATURE REVIEW

Circular economy (CE) is a sustainable alternative to tackle global challenges like climate change, waste, and pollution. The relations, perceptions and behaviors of stakeholders in circular economic activities may form barriers that hinder the circular transition. The promotion of CE requires investigating the interactions and information flow between CE stakeholders from a network perspective. This study revisits the applications of social network analysis (SNA) in promoting CE. Related concepts of CE and the research contents of SNA are reviewed. A bibliometric analysis is conducted to provide a bird’s eye on the research status and trend. On this basis, we summarize the challenges of promoting CE and refine specific problems, around which we review the research status of network modeling methods and statistical measures, information diffusion models, mining methods of perceptions, and social influence analysis. This study outlines the pathways through which SNA contributes to promoting CE, such as through revealing the relational structure and characteristics of stakeholders, forming and changing perceptions of stakeholders, improving behaviors of stakeholders, and examining the development of CE. The lessons learned from the review and future prospects are extensively discussed in combination with the features of the information age from both theoretical and practical perspectives.

Spatial correlation network structure of energy-environment efficiency and its driving factors: a case study of the Yangtze River Delta Urban Agglomeration

Improving energy-environment efficiency is not only a requirement for constructing China’s ecological civilization but also inevitable for achieving sustainable economic and social development. Studies on energy-environment efficiency based on relational data and network perspectives are limited, which hinders the development of collaborative regional emission reduction activities. This study uses the SBM-Undesirable model to measure the energy-environment efficiency of the Yangtze River Delta Urban Agglomeration from 2010 to 2020, adopts a modified gravity model and social network analysis to reveal the structural characteristics of its spatial correlation network, and explores its driving factors through the QAP method. The study found (1) an overall upward trend in energy-environment efficiency but with problems of uneven development. (2) The spatial correlation of energy-environment efficiency shows a complex network structure, with increasing network correlation and strong network stability; the network can be divided into four plates: net benefit, net overflow, two-way spillover, and agent. (3) Differences in industrial structure, environmental regulation, economic development, and technological innovation significantly impact the formation of spatial correlation network of energy-environment efficiency. This study provides a reference for the construction of a cross-regional synergistic mechanism to improve energy-environment efficiency.

All of the same breed? A networking perspective of private-collective innovation

Much research on open source software development has highlighted the best-of-both-worlds benefits generated by private and collective contributions from a broad base of developers. However, these studies have tended to overlook the heterogeneous nature of various developer groups including firm-sponsored developers. To unveil the behavioural differences between developer groups, we expand upon the private-collective innovation model using a networking approach, linking network closure and positional embeddedness with technical contribution. We tested our predictions using a lagged analysis based on communication and networking behaviours on the Linux kernel mailing-list in the production of the Linux operation system over a three-year period. Our findings support our predictions, showing that network closure has an adverse impact on technical contribution. Additionally, the relationship between positional embeddedness and technical contribution follows an inverted U-shape. In addition, high positional embeddedness counteracts the negative influence of extensive network closure on technical contribution. These effects are partly moderated by respective developer groups. Our model and results offer important theoretical and practical implications for community management within the framework of private-collective innovation.

The development of depressive symptoms in older adults from a network perspective in the English Longitudinal Study of Ageing

An increased understanding of the interrelations between depressive symptoms among older populations could help improve interventions. However, studies often use sum scores to understand depression in older populations, neglecting important symptom dynamics that can be elucidated in evolving depressive symptom networks. We computed Cross-Lagged Panel Network Models (CLPN) of depression symptoms in 11,391 adults from the English Longitudinal Study of Ageing. Adults aged 50 and above (mean age 65) were followed over 16 years throughout this nine-wave representative population study. Using the eight-item Center for Epidemiological Studies Depression Scale, we computed eight CLPNs covering each consecutive wave. Across waves, networks were consistent with respect to the strength of lagged associations (edge weights) and the degree of interrelationships among symptoms (centrality indices). Everything was an effort and could not get going displayed the strongest reciprocal cross-lagged associations across waves. These two symptoms and loneliness were core symptoms as reflected in strong incoming and outgoing connections. Feeling depressed was strongly predicted by other symptoms only (incoming but not strong outgoing connections were observed) and thus was not related to new symptom onset. Restless sleep had outgoing connections only and thus was a precursor to other depression symptoms. Being happy and enjoying life were the least central symptoms. This research underscores the relevance of somatic symptoms in evolving depression networks among older populations. Findings suggest the central symptoms from the present study (everything was an effort, could not get going, loneliness) may be potential key intervention targets to mitigate depression in older adults.

Mulsemedia communication research challenges for metaverse in 6G wireless systems

Although humans have five basic senses, sight, hearing, touch, smell, and taste, most multimedia systems today only capture two of them, namely, sight and hearing. With the development of the metaverse and related technologies, there is a growing need for a more immersive media format that leverages all human senses. Multisensory media (Mulsemedia) that can stimulate multiple senses will play a critical role in the near future. This paper provides an overview of the history, background, use cases, existing research, devices, and standards of mulsemedia. Emerging mulsemedia technologies such as extended Reality (XR) and Holographic-Type Communication (HTC) are introduced. Additionally, the challenges in mulsemedia research from the perspective of wireless communication and networking are discussed. The potential of 6G wireless systems to address these challenges is highlighted, and several research directions that can advance mulsemedia communications are identified.

Network analysis of depression and anxiety symptoms and their associations with mobile phone addiction among Chinese medical students during the late stage of the COVID-19 pandemic

Network analysis provides a novel approach to discovering associations between mental disorders at the symptom level. This study aimed to examine the characteristics of the network of depression and anxiety symptoms and their associations with mobile phone addiction (MPA) among Chinese medical students during the late stage of the COVID-19 pandemic. A total of 553 medical students were included. Depression and anxiety symptoms and MPA were measured by the nine-item Patient Health Questionnaire (PHQ-9), the seven-item Generalized Anxiety Disorder Scale (GAD-7), and the Mobile Phone Addiction Index (MPAI), respectively. Central and bridge symptoms were identified with centrality indices and bridge centrality indices. Network stability was examined using the case-dropping procedure. “Uncontrollable worry”, “restlessness” and “nervousness” were the central symptoms in the depression and anxiety network. “Restlessness” and “motor” were the most central bridge symptoms linking depression and anxiety. “Concentration”, “anhedonia” and “sleep” were most strongly associated with MPA. “Uncontrollable worry”, “restlessness”, “nervousness,” and “motor” may be the symptoms for interventions to target in medical students with comorbid depression and anxiety. From a network perspective, depressive symptoms may be more important than anxiety symptoms in medical students with MPA.

Inter-relationships of insomnia and psychiatric symptoms with suicidal ideation among patients with chronic schizophrenia: A network perspective

BackgroundInsomnia is common in patients with schizophrenia, which contributes to worsening psychiatric symptoms and suicidality. We aimed to assess the inter-relationships of insomnia and psychopathology with suicidal ideation (SI) among 1407 Chinese patients with chronic schizophrenia via the network approach. MethodWe used Positive and Negative Syndrome Scale, Insomnia Severity Index, and Beck Scale for Suicidal Ideation to assess psychiatric symptoms, insomnia, and SI, respectively. Lifetime suicidal attempts (SA) were collected. Results(1) The incidence of insomnia, lifetime SI, lifetime SA, and current SI was 13.5% (n = 190), 22.8% (n = 321), 13.5% (n = 190), and 9.7% (n = 136), respectively. (2) Patients with insomnia had worse clinical symptoms and higher suicidal risk. (3) Daytime dysfunction, sleep-related distress, conceptual disorganization, delusions, anxiety, and poor rapport were the core symptoms, while late sleep onset and sleep dissatisfaction emerged as bridge symptoms connecting insomnia and psychopathology. (4) Depressive mood, hallucinations, poor impulse control, guilty feelings, insomnia-related impaired quality of life, and sleep dissatisfaction were directly associated with SI. ConclusionOur findings called for formal assessment of insomnia in patients with schizophrenia, which should cover both nocturnal and daytime insomnia symptoms. Targeted interventions for key symptoms may help reduce insomnia, psychiatric symptoms, and SI in patients with schizophrenia.

Effects of polyethylene microplastics and heavy metals on soil-plant microbial dynamics

Polyethylene (PE) microplastics are emerging pollutants that pose a significant threat to the environment and human health. However, little is known about the effects of PEs on soil‒plant interactions, especially in heavy metal (HM)-contaminated soil. In this study, the effects of PE on rhizosphere soil enzyme activities, microbial interactions and nutrient cycling processes were analyzed from ecological network and functional gene perspectives for the first time. The results indicated that PE-MP addition significantly reduced the biomass of Bidens pilosa L. In addition, the partial increase in carbon, nitrogen, and phosphorus enzyme activities suggested that the effects of PE as a carbon source on microbial functions in HM-contaminated soil should not be ignored. The average path length of bacterial network nodes was found to be higher than that of fungal network nodes, demonstrating that the bacterial ecological network in PE-MP and HM cocontaminated environments has good buffering capacity against changes in external environmental conditions. Furthermore, structural equation modeling demonstrated that particle size and dosage affect soil nutrient cycling processes and that cycling processes are acutely aware of changes in any factor, such as soil moisture, soil pH and soil nitrogen nutrients. Hence, PE-MP addition in HM-contaminated soil has the potential to alter soil ecological functions and nutrient cycles.

Exploring Technology- and Sensor-Driven Trends in Education: A Natural-Language-Processing-Enhanced Bibliometrics Study

Over the last decade, there has been a large amount of research on technology-enhanced learning (TEL), including the exploration of sensor-based technologies. This research area has seen significant contributions from various conferences, including the European Conference on Technology-Enhanced Learning (EC-TEL). In this research, we present a comprehensive analysis that aims to identify and understand the evolving topics in the TEL area and their implications in defining the future of education. To achieve this, we use a novel methodology that combines a text-analytics-driven topic analysis and a social network analysis following an open science approach. We collected a comprehensive corpus of 477 papers from the last decade of the EC-TEL conference (including full and short papers), parsed them automatically, and used the extracted text to find the main topics and collaborative networks across papers. Our analysis focused on the following three main objectives: (1) Discovering the main topics of the conference based on paper keywords and topic modeling using the full text of the manuscripts. (2) Discovering the evolution of said topics over the last ten years of the conference. (3) Discovering how papers and authors from the conference have interacted over the years from a network perspective. Specifically, we used Python and PdfToText library to parse and extract the text and author keywords from the corpus. Moreover, we employed Gensim library Latent Dirichlet Allocation (LDA) topic modeling to discover the primary topics from the last decade. Finally, Gephi and Networkx libraries were used to create co-authorship and citation networks. Our findings provide valuable insights into the latest trends and developments in educational technology, underlining the critical role of sensor-driven technologies in leading innovation and shaping the future of this area.

Improved Link Prediction Method for Maritime Silk Road Shipping Network Using Composite Index

The container shipping network has evolved with the development of trade, but the mechanism of its evolution is still not well studied. Link prediction model is one of the main methods used to study the network evolution. The classic indicators of the link prediction model only consider the structural characteristics of the network, but there are also real factors that can influence the evolution of the network. Therefore, this paper considers the structural characteristics of the network as an endogenous index, which includes common neighbors, Adamic–Adar, resource allocation, and so forth. Considering the factors that may affect the link between the ports, such as sailing distance, operation, economy, and political factors, this paper first develops different attractiveness models and determines an optimal attractiveness model in case analysis. After that, this paper proposes a composite index that combines endogenous and attractiveness indices. Taking the Maritime Silk Road (MSR) shipping network as an example, an optimal attractiveness index and then an optimal composite index are determined. It was found that the prediction accuracy under the composite index is improved compared with single indices, which confirms the effectiveness and superiority of the proposed index. Finally, from the perspective of the overall network, this paper discusses the possible future routes of the shipping network with the aim of providing a reference for the improvement of connectivity of the shipping network and planning new routes for shipping companies.

A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

In this paper, the application and future vision of Artificial Intelligence (AI)-based techniques in microgrids are presented from a cyber-security perspective of physical devices and communication networks. The vulnerabilities of microgrids are investigated under a variety of cyber-attacks targeting sensor measurements, control signals, and information sharing. With the inclusion of communication networks and smart metering devices, the attack surface has increased in microgrids, making them vulnerable to various cyber-attacks. The negative impact of such attacks may render the microgrids out-of-service, and the attacks may propagate throughout the network due to the absence of efficient mitigation approaches. AI-based techniques are being employed to tackle such data-driven cyber-attacks due to their exceptional pattern recognition and learning capabilities. AI-based methods for cyber-attack detection and mitigation that address the cyber-attacks in microgrids are summarized. A case study is presented showing the performance of AI-based cyber-attack mitigation in a distributed cooperative control-based AC microgrid. Finally, future potential research directions are provided that include the application of transfer learning and explainable AI techniques to increase the trust of AI-based models in the microgrid domain.

Exploring the impact of partner match on business model innovation: the mediating role of interfirm dynamic capabilities-based on ecosystem orchestration perspective

PurposeWithin the context of an open innovation business environment, the frequent interaction and coordination activities among heterogeneous partners have a significant impact on enterprises' business model. Nevertheless, fewer empirical research has been made to explore how to match external partners and update organizational dynamic capabilities at an ecosystem level. Therefore, this paper attempts not only to investigate the direct impact of partner match on different business model innovation (BMI) themes (efficiency-centered BMI and novelty-centered BMI) but only to shed light on the pivotal mediating role of interfirm dynamic capabilities.Design/methodology/approachThis paper utilized the methodology of Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate the impact of diverse partner selection criteria and interfirm dynamic capabilities on two distinctive themes of BMI. More than 20 industry clusters with multiple industries were selected as representatives of the creative ecosystem, predominantly from the Yangtze River Delta region. Valid data were collected from 254 managers by both online questionnaires and offline interviews.FindingsThe findings of the study show that different partner match criteria have distinct direct impacts on BMI themes. Partner complementary and partner synergy, deriving from the “task-related criteria”, are significantly correlated with both EBMI and NBMI. Conversely, partner compatibility, deriving from “Partnering-related Criteria”, shows a positive correlation with EBMI but not NBMI. Furthermore, compare the indirect effect on EBMI, the paper’ results demonstrate interfirm dynamic capabilities as mediator can more maximize external benefits to promote NBMI.Practical implicationsThe study findings effectively help enterprises implement different BMI themes. From a management perspective, whether pursuing EBMI or NBMI, enterprises should consciously seek partners who can provide complementary support or share mutual goals across diverse industries. This strategic approach can significantly enhance the opportunities for sustainable and innovative business development. Furthermore, to successfully accomplish NBMI, enterprises must cultivate interfirm dynamic capabilities encompassing a comprehensive range of cross-organizational innovation capacities, such as bolstering organizational learning capability, establishing interactive network platforms to enhance coordination capabilities and engaging in integrative activities to foster a collective mindset.Originality/valueThis paper contributes to the match theory by introducing three critical matching criteria, enabling enterprises to discern partners based on diverse organizational characteristics. Additionally, this paper broadens the scope of the dynamic capability literature by adopting a network perspective to strengthen interaction and relationship mechanisms. The authors primarily elucidate the concept of interfirm dynamic capabilities as a formative higher-order model formed by three sub-capabilities (absorptive capacity, coordination capability and collective mind). Finally, this paper combines matching theory with dynamic capacity theory to the field of BMI, which adds depth and complexity to the existing ecosystem innovation research.

Tolerance Analysis of Cyber-Manufacturing Systems to Cascading Failures

In practical cyber-manufacturing systems (CMS), the node component is the forwarder of information and the provider of services. This dual role makes the whole system have the typical physical-services interaction characteristic, making CMS more vulnerable to cascading failures than general manufacturing systems. In this work, in order to reasonably characterize the cascading process of CMS, we first develop an interdependent network model for CMS from a physical-service networking perspective. On this basis, a realistic cascading failure model for CMS is designed with full consideration of the routing-oriented load distribution characteristics of the physical network and selective load distribution characteristics of the service network. Through extensive experiments, the soundness of the proposed model has been verified and some meaningful findings have been obtained: (1) attacks on the physical network are more likely to trigger cascading failures and may cause more damage; (2) interdependency failures are the main cause of performance degradation in the service network during cascading failures; and (3) isolation failures are the main cause of performance degradation in the physical network during cascading failures. The obtained results can certainly help users to design a more reliable CMS against cascading failures.

Pathological network changes in patients with social anxiety disorder before and after an Internet-based CBT

A network perspective may shed light on the understanding of Internet-based CBT efficacy for social anxiety disorder. Previous cross-sectional evidence revealed a densely interconnected network for individuals with social anxiety. Yet, longitudinal network changes before and after ICBT are lacking. This study aimed to investigate pathological network changes with Graphical Gaussian Model among patients with social anxiety disorder (n = 249). Social phobia scale (SPS) and Social interaction anxiety scale (SIAS) were measured before and after 8 weeks Internet-based CBT. Results revealed the connection between symptom tension when speaking and symptom awkward when being watched was the most robust edges during ICBT interventions. The pathological network benefited from ICBT and exhibited modification in several prominent interconnections. The overall network connectivity continues to exhibit comparable strength after ICBT. This study represents the first examination of social anxiety network changes after patients with SAD completed a systematic ICBT. Changes in critical edges and nodes provide valuable insights for the design and efficacy assessment of ICBT interventions.

An Organizational Sociology of Education: Using Structural, Network, and Ecological Perspectives to Study Schools

Highlighting changes in education and organizational theorizing since the 1950s, this review integrates three perspectives for an organizational sociology of education. The structural perspective focuses on how the formal organization of resources, relationships, and information can influence student outcomes and inequalities through opportunities to learn. The network perspective highlights the role of informal interactions and interpretation as well as social and cultural capital to bring about changes. The ecological perspective illustrates how schools are affected by other schools (horizontal dimension), the educational bureaucracy (vertical dimension), and organizations outside schools (community dimension). An organizational perspective can concretize often abstract sociological topics on stratification, social reproduction, and socialization. The perspective can also reconceptualize often individualistic views on contemporary education issues like student well‐being, teacher shortage, racial inequalities, and school politics. The review ends with a discussion on how to incorporate these organizational perspectives and how they can complement current studies in education, sociology, and public policy.

A study of the effect of influential spreaders on the different sectors of Indian market and a few foreign markets: a complex networks perspective

A study of the effect of influential spreaders on the different sectors of Indian market and a few foreign markets: a complex networks perspective

The network structure of psychopathological and resilient responses to the pandemic: A multicountry general population study of depression and anxiety.

Commonly identified patterns of psychological distress in response to adverse events are characterized by resilience (i.e., little to no distress), delayed (i.e., distress that increases over time), recovery (i.e., distress followed by a gradual decrease over time), and sustained (i.e., distress remaining stable over time). This study aimed to examine these response patterns during the COVID-19 pandemic. Anxiety and depressive symptom data collected across four European countries over the first year of the pandemic were analyzed (N = 3,594). Participants were first categorized into groups based on the four described patterns. Network connectivity and symptom clustering were then estimated for each group and compared. Two thirds (63.6%) of the sample displayed a resilience pattern. The sustained distress network (16.3%) showed higher connectivity than the recovery network (10.0%) group, p = .031; however, the resilient network showed higher connectivity than the delayed network (10.1%) group, p = .016. Regarding symptom clustering, more clusters emerged in the recovery network (i.e., three) than the sustained network (i.e., two). These results replicate findings that resilience was the most common mental health pattern over the first pandemic year. Moreover, they suggest that high network connectivity may be indicative of a stable mental health response over time, whereas fewer clusters may be indicative of a sustained distress pattern. Although exploratory, the network perspective provides a useful tool for examining the complexity of psychological responses to adverse events and, if replicated, could be useful in identifying indicators of protection against or vulnerability to future psychological distress.

Migration Networks Pattern of China’s Floating Population from the Perspective of Complex Network

Migration Networks Pattern of China’s Floating Population from the Perspective of Complex Network