Diverse Network Research Articles

Networked corporate advocacy in a polarized public arena: analyzing discourse networks of U.S. Fortune 500 companies on controversial issues

Abstract This study investigates how U.S. Fortune 500 corporate social advocacy (CSA) discourse on multiple contentious issues is associated with public attention on Twitter/X. Our theoretical framework elucidates three discourse types and conceptualizes the diversity, structure, and stability of discourse coalition networks. Utilizing computational methods and dynamic network analysis of 43,791 corporate tweets over two years, findings reveal a prevalent use of aspirational communication in addressing climate change and healthcare disparities. Functional discourse is predominantly employed in tweets on racial equality and COVID-19 vaccination, while localization discourse is concentrated in tweets about gender/sexuality. Network analysis shows that discourse coalitions exist across industries, with a convergence in discourse types and low temporal stability in network compositions. Most discourse types and features of discourse coalition networks are positively associated with public attention to CSA tweets, though these associations vary across different issues.

Family networks of lesbian and gay people in Switzerland

AbstractObjectiveThe aim of this study was to estimate the relevance of the family of choice hypothesis in family networks of lesbian and gay individuals living in Switzerland and its implications for their social capital.BackgroundOver the past three decades, family scholars have paid more attention to the emergence of family configurations of LGBTQ+ people that extend beyond the nuclear family and blood kin, with voluntary kin and family of choice playing a key role. However, family networks of lesbian and gay individuals remain an unexplored topic in Switzerland, a country that has taken an extended period to implement institutional acknowledgment of lesbian and gay family rights.MethodPersonal network methods were used to map the main types of family networks of lesbian and gay individuals. In collaboration with an association advocating for lesbian and gay families in Switzerland, the study collected ego‐centered network data on their family.ResultsThe results reveal that the nuclear family holds prominence in many family networks of lesbian and gay people included in the sample, with limited involvement of either blood kin or voluntary kin, which has consequences for their family‐based social capital.ConclusionThe family of choice hypothesis is largely rejected. The focus on the nuclear family is consistent with the late legal recognition of same‐sex marriage and parenthood in Switzerland.ImplicationsThe importance of the nuclear family in the family networks of LGBTQ+ people, as well as the relative diversity of such networks, should be considered by professionals dealing with health and social issues, as well as by legislators, policymakers, and organizations working to promote the family rights of lesbian and gay people and their full social acceptance.

Seizure Detection and Lateralization Using Thalamic Deep Brain Stimulator Recordings.

The lack of reliable seizure detection remains a significant challenge for epilepsy care. A clinical deep brain stimulation (DBS) system provides constrained ambulatory brain recordings; however, limited data exist on the use of DBS recordings for seizure detection and lateralization. We present the case of an 18-year-old patient with drug-resistant focal epilepsy, who had seizure detection and lateralization by DBS recordings. Prior stereotactic-EEG, including a thalamus lead, identified independent left orbitofrontal and mesial temporal onset seizures. Notably, low-frequency thalamic ictal power was significantly elevated relative to baseline awake and sleep states. The patient was subsequently implanted with an anterior nucleus of the thalamus DBS system. Postimplantation, low-frequency power-in-band (5.3-10.3 Hz) recordings were initiated. Nursing staff identified four typical clinical seizures during the inpatient DBS recording period. Thalamic DBS trends contained relative peaks that were coincident with each nurse-reported seizure. Peri-ictal power was uniformly maximal ipsilateral to the seizure network. This case demonstrates the feasibility of seizure detection and lateralization by a thalamic DBS system for some individuals, and suggests DBS sensing parameter selection may be guided by thalamic stereotactic EEG. Further research is necessary to assess the generalizability of DBS seizure detection across individuals and diverse seizure networks.

Researchers’ perspectives on the integration of molecular and genomic data into malaria elimination programmes in Africa: a qualitative study

BackgroundMalaria remains a significant public health concern, despite global efforts to combat the disease with highest burden in Africa. Reports of emerging artemisinin partial- resistance in East Africa emphasize the importance of molecular data to guide policy decisions. Hence the need for researchers to collaborate with National control programmes to conduct genomics surveillance of malaria to inform malaria control and elimination policies. This study explored genomic researchers’ views on engaging with national control programmes to aid malaria elimination efforts in Africa.MethodsThis research employed an exploratory qualitative approach to investigate the views and experiences of malaria genomics researchers across 16 member countries of the Pathogen Genomic Diversity Network Africa (PDNA). In-depth interviews were conducted with each PDNA Principal Investigator, which were recorded, and transcribed verbatim. Subsequently, the data were analysed thematically with NVivo 12 qualitative data analysis software.ResultsThe study revealed that majority of malaria genomics researchers focused on understanding the genetic composition and adaptation of the malaria parasite, its vector, and human host. Their investigations delved into areas such as drug and insecticide resistance, parasite evolution, host interactions, human host susceptibility to malaria, diversity of vaccine candidates, and molecular surveillance of malaria. Challenges included limited funding, lack of interest and capacity among National Malaria Control Programmes (NMCP) to use research evidence effectively, and difficulties in communicating data implications to policymakers due to the absence of WHO-certified use cases. Despite these obstacles, researchers expressed a keen interest in forming partnerships with NMCPs to integrate genetic data into malaria control efforts in Africa. They also stressed the importance of enhancing researchers' ability to communicate findings to policymakers and local communities through policy briefs and innovative communication strategies.ConclusionThe study underscores the need to strengthen partnerships between genomic researchers and NMCPs to support malaria elimination in Africa. Furthermore, researchers should create practical frameworks for easy integration into WHO reporting formats to facilitate the use of molecular and genomic data in malaria control programme decision-making.

Phylosymbiosis and Elevated Cancer Risk in Genetically Depauperate Channel Island Foxes.

Examination of the host-associated microbiome in wildlife can provide critical insights into the eco-evolutionary factors driving species diversification and response to disease. This is particularly relevant for isolated populations lacking genomic variation, a phenomenon that is increasingly common as human activities create habitat 'islands' for wildlife. Here, we characterised the gut and otic microbial communities of one such species: Channel Island foxes (Urocyon littoralis). The gut microbiome provided evidence of phylosymbiosis by reflecting the host phylogeny, geographic proximity, history of island colonisation and contemporary ecological differences, whereas the otic microbiome primarily reflected geography and disease. Santa Catalina Island foxes are uniquely predisposed to ceruminous gland tumours following infection with Otodectes cynotis ear mites, while San Clemente and San Nicolas Island foxes exhibit ear mite infections without evidence of tumours. Comparative analyses of otic microbiomes revealed that mite-infected Santa Catalina and San Clemente Island foxes exhibited reduced bacterial diversity, skewed abundance towards the opportunistic pathogen Staphylococcus pseudintermedius and disrupted microbial community networks. However, Santa Catalina Island foxes uniquely harboured Fusobacterium and Prevotella bacteria as potential keystone taxa. These bacteria have previously been associated with colorectal cancer and may predispose Santa Catalina Island foxes to an elevated cancer risk. In contrast, mite-infected San Nicolas Island foxes maintained high bacterial diversity and robust microbial community networks, suggesting that they harbour more resilient microbiomes. Considered together, our results highlight the diverse eco-evolutionary factors influencing commensal microbial communities and their hosts and underscore how the microbiome can contribute to disease outcomes.

A simulated annealing algorithm for randomizing weighted networks.

Scientific discovery in connectomics relies on network null models. The prominence of network features is conventionally evaluated against null distributions estimated using randomized networks. Modern imaging technologies provide an increasingly rich array of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here we propose a simulated annealing procedure for generating randomized networks that preserve weighted degree (strength) sequences. We show that the procedure outperforms other rewiring algorithms and generalizes to multiple network formats, including directed and signed networks, as well as diverse real-world networks. Throughout, we use morphospace representation to assess the sampling behavior of the algorithm and the variability of the resulting ensemble. Finally, we show that accurate strength preservation yields different inferences about brain network organization. Collectively, this work provides a simple but powerful method to analyze richly detailed next-generation connectomics datasets.

Topology Bench: systematic graph-based benchmarking for core optical networks

Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises publicly accessible and ready-to-use topologies, including (a) 105 georeferenced real-world optical networks and (b) 270,900 validated synthetic topologies. Prior research on real-world core optical networks has been characterized by fragmented open data sources and disparate individual studies. Moreover, previous efforts have notably failed to provide synthetic data at a scale comparable to our present study. Topology Bench addresses this limitation, offering a unified resource, and represents a 61.5% increase in spatially referenced real-world optical networks. To benchmark and identify the fundamental nature of optical network topologies through the lens of graph-theoretical analysis, we analyze both real and synthetic networks using structural, spatial, and spectral metrics. Our comparative analysis identifies constraints in real optical network diversity and illustrates how synthetic networks can complement and expand the range of topologies available for use. Currently, topologies are selected based on subjective criteria, such as preference, data availability, or perceived suitability, leading to potential biases and limited representativeness. Our framework enhances the generalizability of optical network research by providing a more objective and systematic approach to topology selection. A statistical and correlation analysis reveals the quantitative range of all of these graph metrics and the relationships between them. Finally, we apply unsupervised machine learning to cluster real-world topologies into distinctive groups based on nine optimal graph metrics using K-means. It employs a two-step optimization process: optimal features are selected by maximizing feature uniqueness through principal component analysis, and the optimal number of clusters is determined by maximizing decision boundary distances via support vector machines. We conclude the analysis by providing guidance on how to use such clusters to select a diverse set of topologies for future studies. Topology Bench, openly available via Dataset 1 (https://zenodo.org/records/13921775) and Code 1 (https://github.com/TopologyBench), promotes accessibility, consistency, and reproducibility.

Antidepressant use in spatial social networks.

Social networks may help individuals maintain their mental health. Most empirical work based on small-scale surveys finds that cohesive social networks are critical for mental well-being, while diverse networks are considered less important. Here, we link data on antidepressant use of 277,344 small-town residents to a nationwide online social network. The data enable us to examine how individuals' mental health care is related to the spatial characteristics of their social networks including their ties in the local community and connections to distant communities. We find that, besides the cohesion of social networks around home, the diversity of connections to distant places is negatively correlated with the probability of antidepressant use. Spatial diversity of social networks is also associated with decreasing dosage in subsequent years. This relationship is independent from the local access to antidepressants and is more prevalent for young individuals. Structural features of spatial social networks are prospectively associated with depression treatment.

Examining the presence and effects of coherence and fragmentation in the Gulf of Maine fishery management network

Natural resource management networks cohere due to mutual dependencies and fragment, in part, due to the perceived risks of interaction. However, research on these networks has tended to accept coherence a priori rather than problematizing dependence, and few studies exist on interorganizational risk perception. This article presents the results of a study operationalizing these concepts and measuring the distribution of three types of dependence (capital, legitimacy, and regulatory) and two types of perceived risk (performance and sanction) among nearly fifty stakeholder groups and organizations participating in the management of fisheries in the binational Gulf of Maine. The analysis reveals an organizationally diverse network with several stakeholder types participating, with communications clustered binationally, with low levels of perceived risk in interacting, and interdependencies cohering the network. The types of interorganizational dependence present varied across dyadic relationships, but legitimacy dependence, based on shared understandings that organizations should work together, was the most present and had the largest effect on collaboration-oriented network traits. Sanction risk was more common than performance risk but had the most substantial negative effect. The results suggest an opportunity for additional studies of interorganizational dependance and perceived risk to operationalize and measure the sources of network coherence and fragmentation and their effect on collaboration.

Vertex clustering in diverse dynamic networks

We present theoretical and experimental results for spatiotemporal graph k-means (STGkM)—a new unsupervised method to cluster vertices within a dynamic network. STGkM finds both short-term dynamic clusters and a “long-lived” partition of vertices within a network whose topology is evolving over time; we first introduced this technique in a recent conference paper. Here, we update our algorithm with a more efficient relaxation scheme, provide additional theoretical results, compare its performance to several other methods, and demonstrate its capabilities on real, diverse datasets. We construct a theoretical foundation to distinguish STGkM from connected components and static clustering and prove results for the stochastic setting for the first time. In addition to our previous experiments on the United States House of Representatives dataset, we report new state-of-the-art empirical results on a dynamic scientific citation network and Reddit dataset. These findings demonstrate that STGkM is accurate, efficient, informative, and operates well in diverse settings. Finally, as previously noted, one of the main advantages of STGkM is that it has only one required parameter: k, the number of clusters; we therefore include an extended analysis of the range of this parameter and guidance on selecting its optimal value. Our data and code are available on Github; see: https://github.com/dynestic/stgkm.

Minimizing Communication Overhead in Decentralized Deep Neural Networks

Minimizing communication overhead in decentralized deep neural network (DNN) training has become a critical challenge, particularly with the increasing adoption of distributed systems for large-scale machine learning tasks. This paper introduces advanced techniques that leverage gradient compression, adaptive sparsification, and hybrid aggregation to optimize communication efficiency while maintaining model accuracy and convergence rates. Experimental results on benchmark datasets such as CIFAR-10 and ImageNet show that the proposed methods reduce communication costs by up to 70% compared to standard approaches while achieving comparable or superior model accuracy. Additionally, scalability tests on diverse neural network architectures highlight the robustness of the approach, demonstrating efficient performance across varying network sizes and computational setups. These findings underscore the potential of the proposed strategies to enable faster, cost-effective, and sustainable decentralized deep learning systems.

Investigating the Functional and Architectural Diversity of Leaf Venation Networks

Investigating the Functional and Architectural Diversity of Leaf Venation Networks

Development of a STEM-based digital learning space platform to enhance students’ mathematical creativity in future learning classrooms

This study focuses on developing a web-based digital learning space (DLS) designed to support a STEM learning model that fosters students’ mathematical creativity. The DLS builds upon traditional learning management systems by introducing customizable elements tailored to individual teaching and learning environments (PTEs and PLEs). These features allow for the formation of diverse learning networks within the DLS. This enhanced version integrates frameworks for STEM learning, engaging activities, instructional videos, quizzes, evaluations, feedback tools, and a chat feature for direct communication with instructors. The research and development process employed a 4D model–define, design, develop, and disseminate–and involved validation from content and media experts, confirming the DLS’s reliability for broader testing. Results indicate that the STEM-based DLS is highly suitable for mathematics education, as shown by validation from experts, practicality tests from users, and effectiveness tests that measured students’ mathematical creativity. This study also found moderate improvement in students’ creative mathematical thinking (Ng = 0.554) within classrooms using the STEM-based DLS, highlighting its effectiveness for enhancing these skills. Thus, this STEM-focused DLS offers a mobile-friendly digital environment that effectively promotes students’ mathematical creativity.

The Microbial Co-infection Interaction Network in Apical Periodontitis with Sinus Tracts

This study aims to characterize the bacterial co-occurrence features and potential interactions associated with the presence of sinus tracts in apical periodontitis in a Chinese population by using 16S rRNA next-generation sequencing (NGS). Thirty-one samples from twenty-six patients were collected from root canals. Following the extraction of the bacterial DNA, the V3-V4 hypervariable regions of the 16S rRNA gene were sequenced. Compositional diversity, prominent taxa and co-occurrence network analysis were compared according to the presence or absence of sinus tracts. The overall microbiota in two groups exhibited distinguished patterns. Actinomyces dominated in samples with sinus tracts while Prevotella was the most abundant in samples without sinus tracts. The major pathogens in sinus tracts exhibited a complex co-occurrence network, in which Pseudomonas formed a distinctive cluster with enriched abundance, and the extensive correlations centered on Desulfovibrio and Pseudoramibacter may suggest novel dependencies. In the network without sinus tracts, the Bacteroidetes and Firmicutes taxa presented close internal associations. The sequencing results confirmed the complexity of the microbiota in AP. The presence of sinus tracts was associated with distinctive infective patterns and complicated microbial co-infection interaction networks. Further investigations should be adopted to elucidate the relationship between the novel interactions and disease progression. Exploring the microbial interactions leads to a better understanding of etiology of apical periodontitis. Utilizing next generation sequencing techniques, our research uncovered the bacterial community structure and observed co-infection networks associated with sinus tracts, providing potential insights for prognosis prediction and targeted therapeutics of persistent inflammation.

Navigating the Storm: Career Strategies for Today's Tumultuous Work Environment

This article provides a strategic framework to help professionals navigate their careers amid today's disruptive workplace dynamics. After outlining three primary challenges—technological disruption, economic uncertainty, and rapid organizational change—that are contributing to a more tumultuous work environment, the article introduces a career navigation model centered around developing advanced and transferable skills, cultivating a diverse professional network, establishing a strong personal brand, and prioritizing personal well-being. Examples from different industries illustrate how individuals can apply the recommended skills, networking, branding, and self-care strategies to proactively guide their career journeys through periods of turbulence. The goal is to equip professionals with research-backed guidance for strategically steering their own career paths during an era defined by disruptions, unpredictability and constant change in the world of work.

Correlates and Brain Substrates of Happiness in Community-Dwelling Older Adults in India.

Happiness has been shown to influence many health-related outcomes in older adults. Identifying correlates and brain substrates of happiness across countries and cultures is an important goal, as the global older adult population continues to increase. We used univariate and multiple regression to examine associations between happiness and several demographic, health, and lifestyle variables in 665 older adults (39% female) from Kerala, India. We also used Bayesian regression to examine associations between cortical thickness and happiness in a subsample of 188 participants that completed MRI scanning. Happiness was significantly associated with several variables. In our multiple regression model, which included all significant univariate predictors, self-rated health, depression, anxiety, apathy, social network size, social network diversity, and social support significantly predicted happiness. Demographic indicators (age, sex, education, marital status, residence, and employment status/type), cognitive impairment, comorbidities, and leisure activities were not significantly associated with happiness in the multiple regression model. Cortical thickness in several brain regions was positively associated with happiness scores, including frontal, temporal, parietal, occipital, and cingulate regions. Understanding the key correlates is critical for identifying both modifiable factors that can be targeted in well-being interventions and fixed characteristics that identify those at-risk for reduced happiness. The widespread pattern of brain regions associated with happiness is consistent with the multifactorial nature of happiness and, given that the regions identified do not overlap with those vulnerable to cortical thinning, can help explain why subjective well-being, unlike other cognitive functions, is largely resistant to age-related decline.

개인의 사회적 관계 다양성이 창의적 성과에 미치는 영향력과 심리적 안전감 및 다양성 지원 분위기의 조절효과 분석

This study aims to explore the impact of intrapersonal diversity, specifically gender diversity in voluntary social relationships, on creativity. While prior research on individual creativity has primarily focused on intrinsic motivation and dispositional characteristics, this study adopts a relational perspective to investigate the influence of diversity in individuals’ social relationships. Unlike traditional diversity research, which often conceptualizes diversity as a group-level structural characteristic, this study focuses on the individual level, specifically the diversity of voluntary social networks, and empirically examines its effects. Additionally, this research takes a contextual approach to analyze when, how, and why intrapersonal relationship diversity affects creativity. Among contextual factors influencing team dynamics, this study focuses on team climate variables, such as psychological safety and diversity climate, as moderators. The hypotheses were tested using a multi-level mixed effect model with a matched sample of 124 supervisor-subordinate dyads from 49 teams across 23 companies. The findings indicate that the hypothesized direct positive relationship between intrapersonal social relationship diversity and creativity was not supported. However, the moderating effects of psychological safety and diversity climate were significant, reinforcing the positive relationship under favorable team conditions. This study contributes to diversity and creativity literature by conceptualizing intrapersonal diversity in social relationships, emphasizing the interaction between individual and contextual factors, and providing practical implications for enhancing creativity in organizational settings.

Federated learning for secure and privacy preserving data analytics in heterogeneous networks

Federated Learning (FL) has emerged as a groundbreaking paradigm enabling collaborative machine learning across distributed nodes without centralizing data, thus addressing critical concerns in security and privacy. This survey explores the application of FL for secure and privacy-preserving data analytics in heterogeneous networks, where diverse devices, data distributions, and network conditions present unique challenges. This paper provides a comprehensive review of recent advancements in FL, focusing on its efficacy in safeguarding sensitive information while enabling effective analytics across varied domains such as healthcare, finance, and IoT systems. The paper delves into key methodologies for achieving privacy preservation, including differential privacy, secure multi-party computation, and homomorphic encryption, while analyzing their performance in dynamic and resource-constrained environments. Additionally, this paper examines strategies for managing heterogeneity, including personalized FL, model aggregation techniques, and adaptive optimization algorithms. Challenges such as scalability, communication efficiency, and adversarial robustness are discussed alongside potential solutions and future research directions. This survey aims to provide researchers and practitioners with an in-depth understanding of the state-of-the-art in FL for secure and privacy-preserving data analytics, fostering innovation and addressing emerging needs in increasingly complex network ecosystems.

Navigating the Lay Referral System for Treatment-Seeking Decisions During Illness in the Digital Age: A Qualitative Study of Adults Living in Slums in Nigeria.

Interaction with a lay referral system (informal networks that provide health advice during illness) influences the treatment-seeking decisions of individuals with perceived healthcare needs. We examined how this occurred in urban slums in Nigeria with scarce formal healthcare and unstable social networks. Using existing theories of social networks and lay referral, we examined the use of these systems for treatment-seeking decisions in slums, including the use of digital technologies. We interviewed 30 adults (aged 18-64) of diverse age, gender, network size and use of digital technologies for health advice in two Nigerian slums. We analysed the data using a thematic inductive-deductive approach. Lay referral was multidimensional: discussion of illness during daily bonding, social demonstration of self and purposeful exchange of support. People limited lay referrals to a few family members and friends, avoiding wider interactions due to mistrust. Use of online sources was scarce due to limited access to smart devices and low digital health literacy. Lay referral motivated timely care seeking but also facilitated unhelpful advice. Slum residents were agentic in their use of their lay referral system. The effectiveness of their agency may be improved if trusted and reliable health advice sources are available in addition to their social network.

An Empirical Analysis of DDoS Attack Detection and Mitigation Techniques: A Comparative Review of Tools and Methods

The availability and integrity of online services and networks are seriously threatened by distributed denial of service (DDoS) assaults. As a result, a plethora of detection and mitigation strategies have been created, all utilizing different instruments and approaches. By comparing various tools and approaches, this study provides an empirical examination of DDoS attack detection and mitigation strategies. We carefully assess the performance of top DDoS detection and mitigation solutions against a range of attack vectors and in diverse network contexts, taking into account their efficacy, efficiency, and accuracy. We examine the benefits, drawbacks, and real-world applications of both open-source and proprietary technologies for network defenders. Additionally, we look at how these tools and techniques adapt to changing threats and talk about new trends and difficulties in the DDoS assault arena. For network security practitioners, researchers, and policymakers looking for an understanding of the state of DDoS defence mechanisms and tactics for strengthening resilience against these disruptive cyber threats, this comparative assessment is an invaluable resource.