Network Measures Research Articles

Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships

Social network analysis and shared-patient physician networks have become effective ways of studying physician collaborations. Assortative mixing or “homophily” is the network phenomenon whereby the propensity for similar individuals to form ties is greater than for dissimilar individuals. Motivated by the public health concern of risky-prescribing among older patients in the United States, we develop network models and tests involving novel network measures to study whether there is evidence of homophily in prescribing and deprescribing in the specific shared-patient network of physicians linked to the US state of Ohio in 2014. Evidence of homophily in risky-prescribing would imply that prescribing behaviors help shape physician networks and would suggest strategies for interventions seeking to reduce risky-prescribing (e.g., strategies to directly reduce risky prescribing might be most effective if applied as group interventions to risky prescribing physicians connected through the network and the connections between these physicians could be targeted by tie dissolution interventions as an indirect way of reducing risky prescribing). Furthermore, if such effects varied depending on the structural features of a physician’s position in the network (e.g., by whether or not they are involved in cliques—groups of actors that are fully connected to each other—such as closed triangles in the case of three actors), this would further strengthen the case for targeting groups of physicians involved in risky prescribing and the network connections between them for interventions. Using accompanying Medicare Part D data, we converted patient longitudinal prescription receipts into novel measures of the intensity of each physician’s risky-prescribing. Exponential random graph models were used to simultaneously estimate the importance of homophily in prescribing and deprescribing in the network beyond the characteristics of physician specialty (or other metadata) and network-derived features. In addition, novel network measures were introduced to allow homophily to be characterized in relation to specific triadic (three-actor) structural configurations in the network with associated non-parametric randomization tests to evaluate their statistical significance in the network against the null hypothesis of no such phenomena. We found physician homophily in prescribing and deprescribing. We also found that physicians exhibited within-triad homophily in risky-prescribing, with the prevalence of homophilic triads significantly higher than expected by chance absent homophily. These results may explain why communities of prescribers emerge and evolve, helping to justify group-level prescriber interventions. The methodology may be applied, adapted or generalized to study homophily and its generalizations on other network and attribute combinations involving analogous shared-patient networks and more generally using other kinds of network data underlying other kinds of social phenomena.

International Friends and Enemies

We examine whether, as countries become more economically dependent on a trade partner, they realign politically toward that trade partner. We use network measures of economic exposure to foreign productivity growth derived from the class of trade models with a constant trade elasticity. We establish causality using two different sources of quasi-experimental variation: China’s emergence into the global economy and the reduction in the cost of air travel over time. In both cases, we find that increased economic friendship causes increased political friendship and that our theory-based network measures dominate simpler measures of trading relationships between countries. (JEL D72, E23, F14, F15, F53, O19, P33)

Research on The Accumulation Law and Treatment Measures of Low-pressure Wet Gas Pipeline Network in A Certain Gas Field

Abstract With the increasing development and utilization of natural gas resources, the operational problems of low-pressure wet gas pipelines are gradually becoming prominent, especially in low-pressure wet gas pipelines where changes in gas pressure and temperature are often accompanied by the precipitation of free water and the formation of accumulated liquid. Liquid accumulation not only increases pipeline friction and reduces transportation efficiency, but may also cause a series of safety issues such as corrosion and hydrate freezing, seriously threatening the stable operation of the pipeline network and the efficient supply of natural gas. Therefore, in-depth research and exploration of the accumulation law of low-pressure wet gas pipeline network and corresponding treatment measures have important practical significance and application value for ensuring the safety production of gas fields, improving transportation efficiency, and extending pipeline life. This article selects a low-pressure wet gas pipeline network in a certain gas field as the research object. Through a detailed analysis of its structural characteristics, wet gas transportation process, and the mechanism of liquid accumulation, the impact of liquid accumulation on the pipeline network system is deeply explored. The study analyzed the structural characteristics of the pipeline network, moisture transportation technology, and the mechanism of liquid accumulation. A numerical simulation model was established using advanced OLGA software, and detailed simulation analysis was conducted on the phenomenon of liquid accumulation under different production conditions. The research results show that changes in pipeline throughput and shutdowns and restarts can have an impact on liquid accumulation, especially an increase in start-up throughput can effectively shorten the equilibrium time of liquid accumulation and increase the liquid flow rate at the pipeline outlet. On this basis, this study further explores the best practices for pipeline cleaning operations, proposes a series of suggestions, and explores reasonable cleaning cycles. The optimization of cleaning operations can not only reduce the accumulation of liquid, but also help improve the operational efficiency and safety of the pipeline network. This study not only provides theoretical guidance for the safe and stable operation of low-pressure wet gas pipelines, but also provides strong technical support for related engineering practices. By deeply understanding the law of liquid accumulation and taking corresponding treatment measures, the frictional resistance along the pipeline can be effectively reduced, and accidents such as corrosion and hydrate freezing can be reduced, thereby ensuring the long-term, efficient, and safe production of the gas field.

Loss reduction technical measures for DG-containing distribution networks based on maximizing loss reduction benefits

Abstract Loss reduction renovation of distribution networks is an important measure to improve the economic operation of distribution networks. The proportion of installed capacity of distributed generation (DG) in distribution networks will have an impact on the solutions of loss reduction measures, and the benefits generated by different solutions of loss reduction measures will differ to a greater or lesser extent. In order to maximize the loss reduction benefits, various loss reduction measures such as distribution network reconfiguration, distribution substation replacement, conductor replacement, and reactive power compensation, etc. were combined in this paper. The objective function of maximizing the loss reduction benefits after 10 years was used for mathematical modeling, and the Coati Optimization Algorithm (COA) was used to select and optimize the loss reduction technical measures. Finally, a distribution network with a distributed power supply was analyzed as an example to verify the effectiveness of the model.

Damage Parameters and Crack Morphology in High Strength Concrete BBR9 Under Dynamic Uniaxial Compressive Loading: An Experimental Study

In recent years, there has been significant growth in the use of high-strength concrete (HSC) in structures designed to withstand extreme events such as collisions, terrorist attacks, and earthquakes. Continuum damage mechanics has been utilized to develop constitutive models that can capture the damage evolution in concrete materials under such conditions. These models must be validated against experimental data obtained under controlled laboratory conditions. Yet, no experimental dataset currently exists that accurately quantifies the damage evolution and its influence on the residual mechanical properties of HSC. The present study aimed at investigating the damage initiation, progression, and morphology of HSC-Baseline Basic Research Mixture 9 (BBR9) when subjected to dynamic uniaxial compressive loading. A Kolsky compression bar system was implemented to introduce distinct damage states in the HSC-BBR9 specimens. Thereafter, the partially damaged specimens were tested to quantify their residual mechanical properties. Accordingly, stiffness-based and strength-based constitutive damage parameters were adopted to propose an indirect quantification of the damage state based on the deterioration of mechanical properties. In addition, the X-ray micro‐computed tomography (micro-CT) technique was utilized to extract measurements of 3D crack networks (e.g., crack volume and surface area) that provide a direct quantification of the damage state based on microstructural evidence. The results demonstrated that the HSC-BBR9 material can maintain its residual mechanical properties into the post-peak regime. In the initial stages of damage, stiffness and strength deteriorate at a proportional rate; however, as damage accumulates, the rate of stiffness degradation increases. Moreover, correlations between constitutive damage parameters and 3D crack measurements were established.

6G optical-RF wireless integration: a review on heterogeneous cellular network channel modeling, measurements, and challenges

6G optical-RF wireless integration: a review on heterogeneous cellular network channel modeling, measurements, and challenges

The spillover effects among financial stress, investor sentiment, and GCC stock markets: evidence under the bearish and bullish market states

ABSTRACT This study delves into an exploration of volatility spillover between financial stress, investor sentiment, and stock market index returns in the Gulf Cooperation Council (GCC) countries using the DCC-GARCH, wavelet coherence, and BEKK-GARCH models. A robustness check, applying Diebold and Yilmaz’s (2012, 2014) methodology, verifies network measures across markets. Significant patterns in financial stress index were observed during crises, with strong co-movement between financial stress, investor sentiment, and stock returns, especially during COVID-19 pandemic. Volatility transmission from stock returns to investor sentiment and the financial stress index was notable in some markets. Additionally, we found a significant volatility transmission from the financial stress index to market returns during periods of bearish stress in Bahrain and Kuwait and bullish stress across all countries. These findings offer insights for investors and fund managers optimizing portfolios and managing risk exposure.

Crypto network

The empirical literature has studied linkages in the cryptocurrency market because knowing how shocks pass from one currency to another helps policymakers and practitioners better counter their propagation in these and related markets. This paper contributes to this literature by proposing a methodology based on Granger causality and network analysis. Using the daily log-returns of 22 cryptocurrencies over the period 2018–2023, I develop a VAR model to infer unidirectional or bidirectional Granger causality among cryptocurrencies. These relationships are then transformed into a directed network and several centrality measures are calculated. The centrality measures are also observed over the years to understand the dynamics of the cryptocurrency network. I find out that each one unit increase in eigencentrality is associated with a 0.22 percent increase in log-returns. Cryptocurrencies are nontrivially connected, and in this sample Cardano, Dogecoin, Gridcoin, and Neo are amongst the most central in the network throughout the period. Some cryptocurrencies, such as Dogecoin or Neo, show decreasing centrality over the years, while others, such as Gridcoin, Litecoin, Namecoin, or Ripple, gain centrality. These results support the idea that the cryptocurrency market is no longer exclusively associated with Bitcoin and lay the groundwork for further study of shock propagation in financial markets.

Simulation and Analysis of Water Quality Improvement Measures for Plain River Networks Based on Infoworks ICM Model: Case Study of Baoying County, China

The water environment of plain river networks can be self-cleaning to a certain extent, but if the wastewater load exceeds a certain threshold, it can disturb the natural balance and cause water pollution. This underlines the importance of water pollution control measures. However, the development of water pollution control measures requires a large number of hydrological and hydrodynamic parameters and the establishment of corresponding relationships through modelling. Therefore, this study mainly used the Infoworks ICM model to construct a detailed hydrological–hydrodynamic water environment analysis model for the Yundong area of Baoying County, Yangzhou City, China, screened the main pollution source areas and pollution time periods of the typical rivers in the study area, and proposed effective improvement measures according to the actual situation of the study area. The results show that after the synergistic effect of multiple measures, the water quality can reach the Class III standard (GB3838-2002). This study can provide a reference for the water environment management and improvement of the plain river network and has good application prospects.

A REVIEW OF MACHINE LEARNING AND FEATURE SELECTION TECHNIQUES FOR CYBERSECURITY ATTACK DETECTION WITH A FOCUS ON DDOS ATTACKS

This study provides a systematic review of machine learning (ML) techniques applied in intrusion detection systems (IDS), with a particular focus on Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT). Following the PRISMA guidelines, a comprehensive search of relevant databases identified 205 articles, from which 68 were selected for detailed analysis. The findings highlight that RF consistently outperforms other models, achieving accuracy rates as high as 99.72% in detecting Distributed Denial of Service (DDoS) attacks due to its ensemble learning approach. SVM, while effective in specific scenarios with binary classification tasks, struggles with scalability and high-dimensional datasets, though feature selection significantly improves its performance. DT models, known for their simplicity and interpretability, are prone to overfitting, but this issue is mitigated when combined with feature selection techniques. The study further emphasizes the importance of feature selection in enhancing IDS accuracy and efficiency across various models. Additionally, ensemble and hybrid methods, which combine multiple ML techniques, offer promising improvements in detection accuracy and real-time performance. These findings underscore the potential of machine learning, particularly through the use of ensemble and hybrid approaches, to significantly improve cybersecurity measures in modern networks.

Operationalizing the social capital of collaborative environmental governance with network metrics

Abstract Background
Social capital is frequently invoked as a reason for engaging in collaborative environmental governance. Yet we have a limited understanding of how collaborative environmental governance mobilizes different types of social capital and how the advantages and costs of social capital accrue for different groups of people. Explicit measures of social capital, such as through social network methods, will help build an understanding of how social capital facilitates collective processes and for whom.
Methods
We reviewed highly cited articles in Web of Science and Scopus using “social capital” as the search term to identify foundational and emergent social capital concepts. In the context of collaborative environmental governance, we operationalized these social capital concepts with network measures drawn from our expertise and highlighted existing empirical relationships between such network measures and collaborative outcomes.
Review results
We identified two different camps of social capital concepts - one based on social relations that could be readily operationalized with social network measures and the other based on actor characteristics that can further contextualize network data. Relational social capital concepts included social relations among actors; the collective social setting in which relations are embedded; and the advantages and costs that social capital confers to individuals and the collective. Social capital concepts based on actor characteristics included socio-cognitions (e.g., trust, norms, identification with a group, shared meanings) and community engagement (e.g., group membership, civic participation, volunteerism). Empirical evidence using social network approaches to measure social capital reveals patterns in relationship building that influence collaborative and other sustainability outcomes.
Discussion
Social network approaches described here may help define and quantify the social capital mobilized by collaborative governance. Additional research is necessary to track the social capital of collaboratives over time, link it to outcomes, and better understand the social justice implications of collaborative governance.

Increasing aerosol optical depth spatial and temporal availability by merging datasets from geostationary and sun-synchronous satellites

Abstract. This comprehensive study analyzed aerosol products from six low-Earth orbit (LEO) and geostationary Earth orbit (GEO) sensors. LEO sensors like the MODerate resolution Imaging Spectroradiometer (MODIS) and VIsible InfraRed Suite (VIIRS) provide one to two daily global measurements, while GEO sensors (Advanced Himawari Imager: AHI, Advanced Baseline Imager: ABI) offer high-frequency data (∼ 10 min) over specific regions. The combination of LEO and GEO capabilities offers expanded coverage of the global aerosol system if aerosol retrievals are applied consistently across all sensors and packaged in an easy-to-use product. The Dark Target aerosol retrieval algorithm was applied to the six sensors, and the resulting Level 2 aerosol optical depth (AOD) products were gridded and merged into a Level 3 quarter-degree latitude–longitude grid with a 30 min temporal resolution, providing the necessary consistency and packaging. Validation of this packaged Level 3 AOD product against Aerosol Robotics NETwork (AERONET) measurements across global locations showcased the merged product's robustness with a correlation coefficient of 0.83, revealing a global mean bias of approximately ±0.05, with 65.5 % of retrievals falling within an expected uncertainty range, underlining the reliability of the dataset. The new gridded Level 3 dataset significantly improved daily global coverage to nearly 45 %, overcoming the limitations of individual sensors, which typically range from 12 % to 25 %. Furthermore, this merged dataset approximates the diurnal cycle of AOD observed by AERONET, thus offering insights into diurnal signatures retrieved elsewhere. The resulting dataset's high spatiotemporal resolution and improved global coverage, especially in regions covered by GEO sensors (Americas and Asia), make it a valuable tool for diverse applications. Tracking aerosol transport from phenomena like wildfires and dust storms is gaining precision, enabling enhanced air quality forecasting and hindcasting. Additionally, the study positions the merged dataset as a significant asset for evaluating and intercomparing regional or global model simulations, which was previously unattainable in such a gridded format. The dataset and fusion framework layout in this study have the potential to include data from recently (future) launched other GEO (FCI, AMI) and LEO (PACE, VIIRS-JPSS) sensors.

How Do Virtual Communities of Practice Enhance Professional Connections and Social Capital?

Communities of Practice (CoP) were created to up-skill educators and to mitigate the disruption to physical therapist assistant (PTA) education because of the COVID-19 pandemic. Understanding CoP involves considering individuals and their interactions, making this project significant for pioneering CoP among PTA educators, and using social network analysis (SNA). The research question for this mixed-methods concurrent triangulation study was "To what extent did the network structure of virtual CoP reflect PTA educators' perceptions of participation and mentorship?" Forty of 60 CoP members participated in this study. We analyzed 8 virtual PTA educator CoP that met over 4 months. Study participants completed a questionnaire about their experiences, engagement, and network connections. Individual and group-level friendship and mentorship network measures were studied using SNA. Qualitative responses were analyzed using the case study design approach. All results were integrated to draw out the complexity of the PTA educator CoP. There was high engagement, with 97.5% (n = 39) reporting they would participate again, and 80% (n = 32) resolved an immediate issue affecting their role. Moreover, 92.5% (n = 37) reported a perception of encouragement with the environment. Study participants reported being mentored by an average of 1.2 individuals (median 0, range 0-5) and serving as mentors to an average of 1.4 individuals (median 0, range 0-7). Two themes, unity and knowledge, emerged through the qualitative analysis. Participation by PTA educators in CoP, whether focused on administration, teaching methods, or clinical education, brings valuable outcomes. Our research supports existing literature in physical therapy education. We discovered that participant engagement, nurturing mentor relationships, encouraging knowledge sharing, and promoting a sense of unity among educators are all important factors.

Multiplex measures for higher-order networks

A wide variety of complex systems are characterized by interactions of different types involving varying numbers of units. Multiplex hypergraphs serve as a tool to describe such structures, capturing distinct types of higher-order interactions among a collection of units. In this work, we introduce a comprehensive set of measures to describe structural connectivity patterns in multiplex hypergraphs, considering scales from node and hyperedge levels to the system’s mesoscale. We validate our measures with three real-world datasets: scientific co-authorship in physics, movie collaborations, and high school interactions. This validation reveals new collaboration patterns, identifies trends within and across movie subfields, and provides insights into daily interaction dynamics. Our framework aims to offer a more nuanced characterization of real-world systems marked by both multiplex and higher-order interactions.

A multi-modal assessment of self-knowledge in adolescents with non-suicidal self-injury: a research domains criteria (RDoC) study.

Adolescence is a critical period for brain development, consolidation of self-understanding, and onset of non-suicidal self-injury (NSSI). This study evaluated the RDoC (Research Domain Criteria) sub-construct of Self-Knowledge in relation to adolescent NSSI using multiple units of analysis. One hundred and sixty-four adolescents assigned female at birth (AFAB), ages 12-16 years with and without a history of NSSI entered a study involving clinical assessment and magnetic resonance imaging (MRI), including structural, resting-state functional MRI (fMRI), and fMRI during a self-evaluation task. For imaging analyses, we used an a priori defined Self Network (anterior cingulate, orbitofrontal, and posterior cingulate cortices; precuneus). We first examined interrelationships among multi-level Self variables. We then evaluated the individual relationships between NSSI severity and multi-level Self variables (self-report, behavior, multi-modal brain Self Network measures), then conducted model testing and multiple regression to test how Self variables (together) predicted NSSI severity. Cross-correlations revealed key links between self-reported global self-worth and self-evaluation task behavior. Individually, greater NSSI severity correlated with lower global self-worth, more frequent and faster negative self-evaluations, lower anterior Self Network activation during self-evaluation, and lower anterior and posterior Self Network resting-state connectivity. Multiple regression analysis revealed the model including multi-level Self variables explained NSSI better than a covariate-only model; the strongest predictive variables included self-worth, self-evaluation task behavior, and resting-state connectivity. Disruptions in Self-Knowledge across multiple levels of analysis relate to NSSI in adolescents. Findings suggest potential neurobiological treatment targets, potentially enhancing neuroplasticity in Self systems to facilitate greater flexibility (more frequently positive) of self-views in AFAB adolescents.

Hidden Markov models for predicting cell-level mobile networks performance degradation

The ubiquitous deployment of Fourth-generation (4G) and Fifth-generation (5G) networks, while expanding user connectivity over a larger geographic area, presents significant challenges for mobile network management. The surge in connected devices increase cell congestion, while user mobility, fading, and interference further deteriorate channel quality. The combined effect of these factors contribute to a gradual degradation in cell and network performance, straining the capabilities of Mobile Network Operators (MNOs) to maintain optimal service quality. The degradation progresses through distinct stages, and ultimately leading to cell outages. Existing cell degradation detection methods, such as key performance indicators (KPIs) monitoring and drive tests, rely on indicators such as signal strength (like reference signal received power (RSRP)), network measurements (like traffic volume and handover requests), and user complaints. However, these methods may not provide real-time insights, leading to delays in identifying and addressing performance issues. This paper proposes a Hidden Markov Model (HMM) approach for proactive cell degradation prediction using real-time time-series KPIs data collected from a telecom operator’s network management system. The NetMax system, a network management system specifically designed for ZTE networks, was used for KPIs data collection. RSRP, 3GPP-recommended signal strength metrics for degradation measure, defines hidden states, while traffic volume and handover requests represent the observable states. The HMM achieves an average prediction accuracy of 93.12%, F1 score of 91.81%, and precision of 92.82% with 23 observation lengths, demonstrating the models’s effectiveness in predicting mobile network’s cell degradation severity.

Social and general cognition are uniquely associated with social connectedness in later life.

The mechanisms by which older adults maintain large, complex social networks are not well understood. Prior work has primarily focused on general cognitive ability (e.g., executive function, episodic memory), largely overlooking social cognition-the ability to process, store, and remember social information. Because social cognition plays a key role in navigating social interactions and is distinct from general cognition, we examined whether general and social cognition uniquely predicted the nature of older adults' personal social networks. Our study leveraged comprehensive measures of general cognition (executive function, episodic memory), social cognition (face memory and dynamic measures of cognitive and affective theory of mind), and a rigorous measure of personal social networks from 143 community-dwelling older adults. We found that, when modeled together and controlling for sociodemographic variables, only executive function and dynamic cognitive theory of mind positively predicted having social networks with relatively unfamiliar, loosely connected others, accounting for 17% of the unique variance in older adults' social connectedness. Interestingly, having a social network comprised primarily of close, tightly knit relationships was negatively associated with affective theory of mind performance. Findings are discussed in the context of the social-cognitive resource framework-which suggests that social cognition may be more engaged in relatively unfamiliar, versus close, interactions. Specifically, our results show that social-cognitive processes may be relatively automatic for individuals whose primary social relationships are very close but may be more strongly engaged for individuals whose interactions include at least some relatively less close relationships. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Insider threat mitigation: Systematic literature review

The increasing prevalence of cybercrime necessitates the implementation of robust security measures. The majority of these attacks are initiated by authorized users who possess knowledge of the system vulnerabilities. Thus, insider attack prevention (ITP) strategies need to be explored to ensure the security of organizations. This review comprehensively examines the existing literature on ITP methods, focusing on recent developments and their implementation in various corporations. This review categorizes and classifies different types of insider attacks and their corresponding prevention and detection techniques. This paper also introduces a multi-tiered activity monitoring model that integrates network, system, and physical security measures to provide comprehensive defense against insider threats. This review also provides a detailed taxonomy that classifies insider threats based on insider type, access level, and targeted security objectives. Finally, future directions for ITP are explored, highlighting the open issues and challenges that need to be addressed.

Towards Easy-to-Monitor Networks: Network Design and Measurement Path Construction

Towards Easy-to-Monitor Networks: Network Design and Measurement Path Construction

Collective flow-evolutionary patterns reveal the mesoscopic structure between snapshots of spatial network

Uncovering the collective behavior of flows among locations is critical to understanding the structure within an ever-changing spatial network. When a network evolves, there may exist subgraphs within which the internal flows generally follow a rule: the change rates of the flow weight are either collectively high or low. Classic network measures such as degree, clustering, and betweenness can be used to quantify the process of network evolution by profiling the overall characteristics over time. However, it remains challenging to elucidate how a spatial network is evolving without looking at structures where collective changes emerge. To bridge this gap, we introduce the concept of the Collective Flow-Evolutionary Pattern (CFEP) as a mesoscopic description for spatial network evolution. Four types of patterns with distinct features are defined to clarify the collective behaviors of the flow-evolutionary characteristics. We provide an analytical framework that utilizes flow change rates between two snapshots of the spatial network to detect CFEPs as optimized flow evolution (evo-groups). Synthetic experiments are presented to validate the method. A case study of large-scale individual mobile positioning data is conducted in the Twin Cities Metropolitan Area, Minnesota, US to demonstrate how CFEP can effectively understand the evolution of human mobility networks.