Social Network Analysis Measures Research Articles

Impacts of Brazilian Green Coffee Production and Its Logistical Corridors on the International Coffee Market

Background: The coffee industry is one of the most important world supply chains, with an estimated consumption of two billion cups daily, making it the most consumed beverage worldwide. Coffee beans are primarily grown in tropical countries, with Brazil accounting for almost 50% of the production. The objective of this study is to examine the Brazilian trade between 2018 and 2022, focusing on state producers, logistical corridors, and importer countries. Methods: The methodology approach revolves around a quantitative method using Social Network Analysis measures. Results: The results reveal a massive concentration in local production (99.5%—Minas Gerais), port movements (99.9%—Santos, Itaguai, and Rio de Janeiro), and country buyers (80.9%—the United States, United Kingdon, and Japan). Conclusions: The study concludes that the Brazilian green coffee supply chain relies on a fragile and overloaded logistical network. Due to that, this study indicates that the stakeholders and decision-makers involved must consider this high concentration of production in some areas and companies. They must also address the bottlenecks in logistical corridors and the fierce competition involved in acquiring and processing Brazilian coffee production because these factors can drastically affect the revenue of the companies operating in this sector.

A data envelopment analysis model for opinion leaders’ identification in social networks

Through Online Social Networks (OSNs) such as Instagram, X (Twitter), and Facebook, employing Opinion Leaders (OLs) is becoming integral to companies’ strategies for influencing the public. The graph-based methods are one of the most important approaches for finding OLs in OSNs. Social Network Analysis (SNA)-based OLs finding methods deal with a considerable amount of data due to using entire relationships between all of the users in a network, which makes the algorithms time-consuming. Our main goal is to introduce a new method of OLs discovery that works with fewer data and maintains or improves performance metrics. Consequently, a new application of the Data Envelopment Analysis (DEA) method is presented here for OLs identification in social media. Another contribution of this paper is introducing a new framework (OL-Finder Evaluator or OLFE) for validating the OLs’ detection algorithms under imbalanced datasets. DEA methods, when compared with SNA methods, have the advantage of being able to apply over non-graph-based datasets and to work with substantially smaller datasets. In contrast, SNA methods require transparent relationships between people. In this study, we compare both DEA (including CCR and BCC) and SNA measures (including “Betweenness (BC),” “Degree (DC),” “Page Rank (PRC),” “Closeness (CC),” and “Eigenvector (EC)” centralities) on a real Instagram network for OLs detection. Compared with SNA, our proposed method can identify OLs with considerably fewer data. Besides the advantages of DEA for time-saving, close competition exists between the DEA and the SNA methods. On average, DEA performs better in accuracy, precision, recall, and F1-score performance metrics.

Representation of the hierarchical and functional structure of an ambulatory network of medical consultations through Social Network Analysis, with an emphasis on the role of medical specialties.

Ambulatory Health Care Networks (Amb-HCN) are circuits of patient referral and counter-referral that emerge, explicitly or spontaneously, between doctors who provide care in their offices. Finding a meaningful analytical representation for the organic and hierarchical functioning of an Amb-HCN may have managerial and health policymaking implications. We aimed to characterize the structural and functional topology of an Amb-HCN of a private health insurance provider (PHIP) using objective metrics from graph theory. This is a cross-sectional quantitative study with a secondary data analysis study design. A Social Network Analysis (SNA) was conducted using office visits performed between April 1, 2021 and May 15, 2022, retrieved from secondary administrative claim databases from a PHIP in Belo Horizonte, Southeastern Brazil. Included were beneficiaries of a healthcare plan not restricting the location or physician caring for the patient. A directional and weighted network was constructed, where doctors were the vertices and patient referrals between doctors, within 7-45 days, were the network edges. Vertex-level SNA measures were calculated and grouped into three theoretical constructs: patient follow-up (aimed at assessing the doctor's pattern of patient follow-up); relationship with authorities (which assessed whether the doctor is an authority or contributes to his or her colleague's authority status); and centrality (aimed at positioning the doctor relative to the network graph). To characterize physician profiles within each dimension based on SNA metrics results, a K-means cluster analysis was conducted. The resulting physician clusters were assigned labels that sought to be representative of the observed values of the vertex metrics within the clusters. Overall, 666,263 individuals performed 3,863,222 office visits with 4,554 physicians. A total of 577 physicians (12.7%) had very low consultation productivity and contributed very little to the network (i.e., about 1.1% of all referrals made or received), being excluded from subsequent doctor profiles analysis. Cluster analysis found 951 (23.9%) doctors to be central in the graph and 1,258 (31.6%) to be peripheral; 883 (22.2%) to be authorities and 266 (6.7%) as seeking authorities; 3,684 (92.6%) mostly shared patients with colleagues, with patient follow-up intensities ranging from weak to strong. Wide profile dispersion was observed among specialties and, more interestingly, within specialties. Non-primary-care medical specialties (e.g., cardiology, endocrinology etc.) were associated with central profile in the graph, while surgical specialties predominated in the periphery, along with pediatrics. Only pediatrics was associated with strong and prevalent (i.e., low patient sharing pattern) follow-up. Many doctors from internal medicine and family medicine had unexpectedly weak and shared patient follow-up profiles. Doctor profiles exhibited pairwise relationships with each other and with the number of chronic comorbidities of the patients they treated. For example, physicians identified as authorities were frequently central and treated patients with more comorbidities. Ten medical communities were identified with clear territorial and specialty segregation. Viewing the Amb-HCN as a social network provided a topological and functional representation with potentially meaningful and actionable emerging insights into the most influential actors and specialties, functional hierarchies, factors that lead to self-constituted medical communities, and dispersion from expected patterns within medical specialties.

Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer

Colorectal cancer (CRC) is a primary global health concern, and identifying the molecular pathways, genetic subtypes, and mutations associated with CRC is crucial for precision medicine. However, traditional measurement techniques such as gene sequencing are costly and time-consuming, while most deep learning methods proposed for this task lack interpretability. This study offers a new approach to enhance the state-of-the-art deep learning methods for molecular pathways and key mutation prediction by incorporating cell network information. We build cell graphs with nuclei as nodes and nuclei connections as edges of the network and leverage Social Network Analysis (SNA) measures to extract abstract, perceivable, and interpretable features that explicitly describe the cell network characteristics in an image. Our approach does not rely on precise nuclei segmentation or feature extraction, is computationally efficient, and is easily scalable. In this study, we utilize the TCGA-CRC-DX dataset, comprising 499 patients and 502 diagnostic slides from primary colorectal tumours, sourced from 36 distinct medical centres in the United States. By incorporating the SNA features alongside deep features in two multiple instance learning frameworks, we demonstrate improved performance for chromosomal instability (CIN), hypermutated tumour (HM), TP53 gene, BRAF gene, and Microsatellite instability (MSI) status prediction tasks (2.4%–4% and 7–8.8% improvement in AUROC and AUPRC on average). Additionally, our method achieves outstanding performance on MSI prediction in an external PAIP dataset (99% AUROC and 98% AUPRC), demonstrating its generalizability. Our findings highlight the discrimination power of SNA features and how they can be beneficial to deep learning models’ performance and provide insights into the correlation of cell network profiles with molecular pathways and key mutations.

The geographical dynamics of global R&D collaboration networks in robotics: Evidence from co-patenting activities across urban areas worldwide.

The focus of this study is on the geography of robotics Research and Development (R&D) activities. The objectives are, first, to identify hotspots in robotics R&D worldwide, and second, to characterise structures and dynamics of global robotics R&D collaboration networks through detailed geographical lenses of global urban areas. We use patents as marker for R&D activities, and accordingly focus on technologically oriented R&D, drawing on information from patents applied for between 2002 and 2016. We employ an appropriate search strategy to identify relevant robotics patents based on detailed levels of the Cooperative Patent Classification (CPC) and assign patents to more than 900 global urban areas based on the inventor addresses. The co-patent networks are examined from a Social Network Analysis (SNA) perspective by means of robotics co-patents, contributing to a global network where urban areas are the nodes inter-linked by joint inventive activities recorded in robotics patents. Global SNA measures illustrate structures and dynamics of the network as a whole, while local measures indicate the specific positioning and roles of urban areas in the network. The results are original in characterising the global spatial emergence of this generic new industry, highlighting prominent urban hotspots in terms of specialisation in robotics R&D, pointing to a global shift reflected by the increasing role of emerging economies, in particular China. The global robotics R&D has grown significantly both in total patenting and also in terms of R&D collaboration activities between urban areas. Also, for the networks, growth is not equally distributed, but is rather characterised by significant spatial shifts, both in terms of cities declining or climbing up the specialisation ranking, but even more in terms of the spatial network structure.

Network analysis of innovation mentor community of practice

PurposePositive effect of knowledge sharing (KS) on innovation has come to the fore and government-supported innovation and mentoring communities or mentor networks have become widespread. This article aims to examine the community connectedness and mentors' preferences for professional competency-based KS of such innovation community of practice networks (CoPNs).Design/methodology/approachThe paper constructs a directed weighted CoPN model with a node-attribute-based novel fingerprint edge weights. Based on the CoPN, Social Network Analysis (SNA) metrics and measures including Giant Component (GC) were proposed and analyzed to identify mentors' connectedness preferences. The fingerprint was proposed as a novel binarized node attribute of competence. Jaccard similarity of fingerprints was proposed as edge weights to reveal correlations between competences and preferences for KS.FindingsThe work opted to conduct a survey of 28 innovation mentors to measure a CoPN. Both a name generator question and a second set of questions were employed to invite respondents to name their collaborators and indicate their professional competence. SNA metrics result in differing values for GC and the rest, which lead us to focus on GC to reveal salient metrics of connectedness. Jaccard similarity analysis results on GC demonstrate that mentors collaborate in an interdisciplinary manner.Originality/valueBased on the CoPN, the methods proposed may be effective in predicting preferred relationships for interdisciplinary collaborations, providing the managers with an analytical decision support tool for KS in practice.

Opinion Leaders for Information Diffusion Using Graph Neural Network in Online Social Networks

Various opportunities are available to depict different domains due to the diverse nature of social networks and researchers' insatiable. An opinion leader is a human entity or cluster of people who can redirect human assessment strategy by intellectual skills in a social network. A more comprehensive range of approaches is developed to detect opinion leaders based on network-specific and heuristic parameters. For many years, deep learning–based models have solved various real-world multifaceted, graph-based problems with high accuracy and efficiency. The Graph Neural Network (GNN) is a deep learning–based model that modernized neural networks’ efficiency by analyzing and extracting latent dependencies and confined embedding via messaging and neighborhood aggregation of data in the network. In this article, we have proposed an exclusive GNN for Opinion Leader Identification (GOLI) model utilizing the power of GNNs to categorize the opinion leaders and their impact on online social networks. In this model, we first measure the n-node neighbor's reputation of the node based on materialized trust. Next, we perform centrality conciliation instead of the input data's conventional node-embedding mechanism. We experiment with the proposed model on six different online social networks consisting of billions of users’ data to validate the model's authenticity. Finally, after training, we found the top-N opinion leaders for each dataset and analyzed how the opinion leaders are influential in information diffusion. The training-testing accuracy and error rate are also measured and compared with the other state-of-art standard Social Network Analysis (SNA) measures. We determined that the GNN-based model produced high performance concerning accuracy and precision.

The ties that bind: Social network analysis describes the social element of medical workforce recruitment to rural/remote Australia

We present the argument that medical recruitment to a previously under-recruited remote town was effected through what Social Network Analysis (SNA) measures as “brokerage” which operates amidst “structural holes”. We proposed that medical graduates being generated by the national Rural Health School movement in Australia were particularly affected by the combined effect of workforce lacks (structural holes) and strong social commitments (brokerage) – all key SNA concepts. We therefore chose SNA to assess whether the characteristics of RCS-related rural recruitment had feature that SNA might be able to identify, as operantly measured using the industry-standard UCINET's suite of statistical and graphical tools. The result was clear. Graphical output from the UCINET editor showed one individual as being central to all recently recruited doctors to one rural town with recruitment issues like all the others. The statistical outputs from UCINET characterised this person as the single point of most connections. The real-world engagements of this central doctor were in accord with the description of brokerage, a core SNA construct, relationship with reported the reason for these new graduates both coming and staying in town. SNA thus proved fruitful in this first quantification of the role of social networks in drawing new medical recruits to particular rural towns. It allowed description at the level of individual actors with a potent influence on recruitment to rural Australia. We propose these measures could be helpful as key performance indicators for the national Rural Clinical School programme that is generating and distributing a large workforce in Australia, which appears from this work to have a strong social basis. This redistribution of medical workforce from urban to rural is needed internationally.

Sport Prosumer Networks: Capital and Value of American Sports During Covid-19.

Prosumption capital is underexplored within social media sites, especially within sports. This article explores how the Covid-19 disruptions were used to extract prosumption capital from Twitter. Adopting an economic sociology perspective to measure prosumption capital, 2.3 million tweets were analyzed across the National Basketball Association, Major League Baseball, National Hockey League, and Major League Soccer sports properties. This article applies social network analysis measures, indegree, domain, and proximity prestige to measure prosumption capital and shows how media organizations and other public figures capitalized on the Covid-19 disruptions. It also shows how the structure and those capitalizing through prosumption on Twitter are similar across the sports properties.

Investigating the Structure of the Internet of Things Patent Network Using Social Network Analysis

The Internet of Things (IoT) makes the world around us more intelligent and more responsive by developing cyber–physical systems. The IoT is about to change how we live and how we work. It is essential to have a perspective on this technology’s innovation path to creating more novel applications. Due to increasing Internet connectivity through smartphones, IoT is expected to be an all-pervasive technology, and lately, its patents have grown significantly. Patents contain valuable information, especially including the path of utilizing IoT in different fields. Patent analysis provides valuable information about innovation in IoT technology. This article performs a patent analysis based on the international patent classification (IPC) co-occurrence network in different patents. This network is analyzed by social network analysis (SNA) algorithms. In addition, the most critical nodes are identified by SNA measures and the TOPSIS method. The findings illustrate the structure of the innovation path of IoT, and they will help create more applications and combinations of IoT with other fields. These results provide a guideline that shows how steps for innovation should start.

An entropy-based method to control COVID-19 rumors in online social networks using opinion leaders

- In the ongoing COVID-19 pandemic, people spread various COVID-19-related rumors and hoaxes that negatively influence human civilization through online social networks (OSN). The proposed research addresses the unique and innovative approach to controlling COVID-19 rumors through the power of opinion leaders (OLs) in OSN. The entire process is partitioned into two phases; the first phase describes the novel Reputation-based Opinion Leader Identification (ROLI) algorithm, including a unique voting method to identify the top-T OLs in the OSN. The second phase describes the technique to measure the aggregated polarity score of each posted tweet/post and compute each user's reputation. The empirical reputation is utilized to calculate the user's trust, the post's entropy, and its veracity. If the experimental entropy of the post is lower than the empirical threshold value, the post is likely to be categorized as a rumor. The proposed approach operated on Twitter, Instagram, and Reddit social networks for validation. The ROLI algorithm provides 91% accuracy, 93% precision, 95% recall, and 94% F1-score over other Social Network Analysis (SNA) measures to find OLs in OSN. Moreover, the proposed approach's rumor controlling effectiveness and efficiency is also estimated based on three standard metrics; affected degree, represser degree, and diffuser degree, and obtained 26%, 22%, and 23% improvement, respectively. The concluding outcomes illustrate that the influence of OLs is exceptionally significant in controlling COVID-19 rumors.

Stakeholder Network Analysis for Front-of-Pack Labeling in China.

BackgroundFront-of-pack (FoP) labeling on packaged foods is recommended by the World Health Organization (WHO) to reduce diet-related non-communicable diseases, but it has not yet been implemented in China. The introduction of FoP labeling is driven by multiple institutions and stakeholders. This study aimed to identify key institutional actors involved in FoP labeling and describe links between actors to support future FoP labeling policies and programmes in China.MethodsThe Net-Map method was adopted. We conducted Net-map activities with eight participants using face-to-face interviews between November 2020 and May 2021. Participants were asked to identify actors involved in the development and implementation of FoP labeling in China, describe networks among actors according to pre-defined link types (command, dissemination, funding, and technical assistance), and estimate influence of each actor within the FoP labeling landscape. Social network analysis measures of cohesion and centrality were used to describe each network. Gephi software was used for social network analysis and network visualization.ResultsForty-one unique actors were identified across seven actor categories including government agencies (n = 14), technical support agencies (n = 7), professional associations (n = 10), food industry groups (n = 2), media groups (n = 4), international organizations (n = 3), and a consumer group (n = 1). Weighted influence scores among actors ranged from 0.13 to 3.13. The Department of Food Safety Standards, Risk Surveillance and Assessment of the National Health Commission (DFSSRSA of NHC) was the actor with the highest weighted influence score, followed by the Bureau of Disease Prevention and Control of National Health Commission, Chinese Nutrition Society, and the National Institute for Nutrition and Health of Chinese Center for Disease Control and Prevention. DFSSRSA of NHC played a central role in both command and technical assistance networks. State-owned media had the greatest betweenness and outdegree centrality in the dissemination network. The Chinese Nutrition Society was in the central position and provided funding to diverse actors in the funding network.ConclusionsA variety of multisectoral actors have an interest in the introduction of FoP labeling policies and programmes. Effectively engaging key actors identified in this study can generate a multisectoral commitment to advance FoP labeling policies and programmes in China.

How do urban reciprocal support network legacies matter to improve the resiliency of urban informal livelihoods?

• Collaboration and resource mobilization of reciprocal support network legacies vis-à-vis flood disaster management are observed in urban informal settlement areas. • The evolution and spatial changes of reciprocal support network legacies are traced at different flood inundation phases. • Reciprocal support network legacies have effectively enabled resiliency in flood affected communities. • Affected livelihoods have been revivified by reciprocal support networks. Reciprocal support networks have become increasingly important in healing disaster wounds of adversely affected communities and improving their resilience. This paper demonstrates the empirical and credible evidence of social support network legacies and their characteristics and evolutionary patterns on flood disaster preparedness and recovery from flooding events, focusing on informal urban settlement areas of the city of Colombo, Sri Lanka. This study utilizes social network analysis measures such as degree density, closeness, and betweenness centralities for analyzing behaviors of reciprocal support networks. A key finding is revealed that social collaboration helps to revive urban informal livelihoods affected by the flood in effective ways. This study also demonstrates that the provision of information, activities such as evacuation, moving belongings from and to affected households, provision of food, water, and other basic needs including health supports, shelter, emotional supports, and cleaning contaminated households and public places have played a significant role in flood disaster preparedness vis-à-vis improving urban resiliency and recovery of urban informal livelihoods. In contrast, the involvements of flood disaster supportive network actors such as relatives, friends, neighbors, admin officials, and volunteers have been changed over the flood phases along with improving urban resilience.

Impact of corporate network position on strategic risk and company’s performance – evidence from Poland

PurposeThe purpose of this study is to investigate the links between a company’s position in a corporate network with its financial performance and strategic risk in the context of the largest Central European stock market.Design/methodology/approachThis study integrates the theory of social network analysis (SNA) with corporate governance theory with a special focus on resource dependence theory. Using the framework of network social analysis, the authors use network measures of social capital and embeddedness.FindingsThe results of studying companies listed on the Polish stock exchange indicate that a company’s corporate network position has a significant negative impact on strategic risk while having no influence on its financial performance. The research also highlights the importance of a firm’s corporate governance model for both performance and strategic risk.Research limitations/implicationsThe data collected, and SNA measures used made it possible to conduct a cross-sectional study. Compared to longitudinal studies, this type of study has a couple of disadvantages addressed in the paper. In the future, the dependencies observed in this study should be tested using longer-term data.Originality/valueTo the best of the author’s knowledge, this is the first paper integrating the corporate personal and capital networks to test risk and performance dependencies in the context of Poland’s corporate governance model. The findings and conclusions can also be applied to analyzing Central and Eastern Europe stock markets.

Towards a new methodological approach to social historiography of translation

AbstractIn an attempt to appreciate the contribution that social network analysis (SNA) might offer to translation historiography, two main approaches are presented and discussed in this study: explanatory SNA and exploratory SNA. The former is more concerned with SNA measures while the latter deals with three potential narratives of social networks. The aim is to employ SNA in diachronic and synchronic dimensions of literary translation publishing historiography in Iran from 1991 to 2010, a micro-macro framework that seamlessly integrates agents’ relationships, visualization and network analysis techniques to explore the impact of ideological-political shifts on the quantity as well as quality of major agents’ relations. Furthermore, the study attempts to explore how the synergy between Giddens’ Structuration Theory (GST) and SNA can support a deeper and more empirically grounded understanding of translation historiography. The goal of the study is both methodological and scientific. The results of SNA graphical outputs suggest that there is a significant relationship between the structure of relationships in fiction publishing field and the dominant political discourse in Iran.

Patterns of student collaborative learning in blended course designs based on their learning orientations: a student approaches to learning perspective

This study combines research methods from student approaches to learning research and social network analysis (SNA) to examine patterns of students’ collaborative learning based on their learning orientations amongst 193 postgraduates enrolled in a blended course. The study identified two distinct learning orientations, namely ‘understanding’ and ‘reproducing’, which differed in approaches to learning through inquiry, approaches to using online learning technologies, perceptions of the online workload, and academic outcomes. On the basis of students’ learning orientations and their choice of whether to collaborate and with whom to collaborate, five networks representing five patterns of collaborative learning were found. From these, two did not reveal any collaboration (Understanding Alone and Reproducing Alone networks); and three revealed collaborations (Understanding Collaboration, Mixed Collaboration, Reproducing Collaboration networks). A range of SNA measures were calculated and revealed different features of the three collaboration networks. Viewed together, the combined methodologies suggest that the Understanding Collaboration network has more desirable features of collaboration, such as the intensity of collaboration, having closely knitted groups who tended to seek out and welcome peers and who tended to engage more often in both face-to-face and online modes. The study suggests that helping students adjust their learning orientations, designing some compulsory collaborative assessment tasks, and configuring the composition of collaborative groups are productive strategies likely to improve students’ experiences of collaborative learning.

Automatically interactive group VIKOR decision making mechanism based on BSO-SNA

In order to improve the reliability of alternative ranking and group consensus adaptively, this paper develops an automatically interactive group decision-making (GDM) framework under the VIKOR environment. The proposed framework contains an interaction network mechanism based on social network analysis (SNA) and an opinion interaction mechanism based on Brain Strom Optimization (BSO) algorithm. Firstly, to connect isolated decision-makers (DMs), interaction relationships among DMs are built based on the SNA and similarity measurement. Secondly, the BSO algorithm is introduced to design the opinion interaction mechanism, including subgroup identification model and preference iteration model. The preference iteration model contains an adaptive preference update rule (UR), iteration stop rule (ISR), and solution selection rule (SSR) to enhance group consensus adaptively rather than force DMs to change the preference. Thirdly, the proposed interactive group VIKOR (G-VIKOR) method is applied in SARS-cov-2 nucleic acid detection sites evaluation. Finally, consistency and comparison analysis are developed to illustrate the validity and robustness of evaluation results.

Evaluating Inclusiveness in Multi‐Sector Community Health Networks: The Case of Tribal Organizations

Research ObjectiveThere is growing evidence that strong multi‐sector networks of community organizations can reduce health risks and improve outcomes for historically marginalized and underserved populations. Multiple studies of community networks document the advantages of membership inclusiveness and diversity. Over 70% of American Indian and Alaska Native (AIAN) U.S. residents live in urban areas that are not part of tribal lands. Community health networks serving these areas have the potential to engage with tribal organizations in order to better serve AIAN residents, however, the prevalence of tribal organization engagement in these networks is unknown. This study is the first to quantify variation in network inclusiveness regarding tribal organizations across a large national sample of communities, and to identify predictors of inclusion.Study DesignWe used 2018 National Longitudinal Survey of Public Health Systems (NALSYS) data, a nationally‐representative cohort of U.S. public health systems. Tribal engagement was measured as a binary indicator for whether the local public health official reported tribal organization involvement in at least one of 19 essential public health activities. NALSYS data were linked to the American Community Survey, NACCHO's profile survey, Indian Health Services and Tribal Epidemiology Center facility data, and U.S. Census Bureau reservation land data. Social network analysis measures were computed to indicate the extent of tribal organization participation in community networks and to identify what sectors and social services were most likely to engage with tribal organizations. Linear probability models were used to estimate predictors of tribal engagement. Covariates included county‐level variables for AIAN population, distance from a reservation land, presence of a tribal‐serving health facility, and multiple socioeconomic and demographic variables.Population StudiedA stratified random sample of local public health agencies was surveyed with a 71% response rate, yielding 731 respondents that serve approximately 70% of the US population. To account for agencies serving multiple counties (10% of respondents), we disaggregated data to the county‐level (n = 1051).Principal FindingsLocal health officials in 15.7% of counties reported that their networks included tribal organization engagement in at least one public health activity. Among counties with a tribal‐serving health facility located in the region (n = 329), 30.7% of networks included tribal organization engagement. Top activities for engagement with tribal organizations included assessing and prioritizing community health needs, maintaining communication networks, and investigating adverse health events. Tribal organizations had strongest ties with hospitals and community health centers, and organizations that provide housing services, and services for Veterans and people with disabilities. The likelihood of tribal engagement increased significantly with the size of the AIAN population in urban communities (6.2 percentage points, p < 0.001), presence of a tribal‐serving health facility in the region (12.4 percentage points, p < 0.001), and geographic proximity to reservation land (p < 0.05).ConclusionsThe vast majority of community health networks do not report engagement with tribal organizations. Even when tribal‐serving health facilities are present, reported engagement with tribal organizations remains low.Implications for Policy or PracticeLocal public health agencies and their community partners must prioritize efforts to include tribal organizations in their collaborative networks, in order to address persistent AIAN health disparities and emerging health threats such as COVID‐19.Primary Funding SourceThe Robert Wood Johnson Foundation.

The structural architecture of international industry networks in the global economy.

Globalisation continuously produces novel economic relationships mediated by flows of goods, services, capital, and information between countries. The activity of multinational corporations (MNCs) has become a primary driver of globalisation, shaping these relationships through vast networks of firms and their subsidiaries. Extensive empirical research has suggested that globalisation is not a singular process, and that variation in the intensity of international economic interactions can be captured by ‘multiple globalisations’, however how this differs across industry sectors has remained unclear. This paper analyses how sectoral variation in the ‘structural architecture’ of international economic relations can be understood using a combination of social network analysis (SNA) measures based on firm-subsidiary ownership linkages. Applying an approach that combines network-level measures (Density, Clustering, Degree, Assortativity) in ways yet to be explored in the spatial networks literature, a typology of four idealised international network structures is presented to allow for comparison between sectors. All sectoral networks were found to be disassortative, indicating that international networks based on intraorganisational ties are characterised by a core-periphery structure, with professional services sectors such as Banks and Insurance being the most hierarchically differentiated. Retail sector networks, including Food & Staples Retailing, are the least clustered while the two most clustered networks—Materials and Capital Goods—have also the highest average degree, evidence of their extensive globalisations. Our findings suggest that the multiple globalisations characterising international economic interactions can be better understood through the ‘structural architecture’ of sectoral variation, which result from the advantages conferred by cross-border activity within each.

Examining Social Networks in Text Messages About Nursing Home Resident Health Status.

Social network analysis (SNA) uses quantitative methods to analyze relationships between people. In the current study, SNA was applied in two nursing homes (NHs) to describe how health care teams interact via text messages. Two data sources were used: (a) a Qualtrics® survey completed by advanced practice RNs containing resident transfer data, and (b) text messages from a secure platform called Mediprocity™. SNA software was used to generate a visual representation of the social networks and calculate quantitative measures of network structure, including density, clustering coefficient, hierarchy, and centralization. Differences were found in the low and high transfer rate NHs for all SNA measures. Staff in the NH with low transfer rate had greater decision-making interactions, higher information exchange rates, and more individuals communicating with each other compared to the high transfer rate NH. SNA can be applied to examine communication patterns found in text messages occurring around the time of NH resident transfers. [Journal of Gerontological Nursing, 47(7), 16-22.].