Network Analysis Techniques Research Articles

Research Themes, Trends and Future Priorities in the Field of Climate Change and Health: A Review

Climate change is one of the biggest threats to human living and health in the 21st century. Whilst a large number of papers have been published addressing the health impact of climate change, there is a lack of comprehensive bibliometric analysis in the crosscutting field. This study evaluated the global scientific output of research in the field of climate change and health between 1990 and 2020, based on the Web of Science Core Collection database. Research themes were identified using a social network analysis technique based on author keywords. Research trends were assessed by the change in overall publication number and the percentage of publications in each research theme. Articles were further categorized by the availability of funding and author affiliation to compare the difference between developed and developing countries. Results showed that the research output in the field of climate change and health has increased dramatically in the past 30 years, mainly dominated by researchers in developed countries. The percentage of research receiving funding was found to be the lowest in those published by developing countries only and the highest in those published by the collaboration of developed and developing countries. A total of nine major research themes was identified. Research related to ‘risk assessment and adaptation’, ‘sustainable development’ and ‘infectious diseases’ were relatively underfunded. A significant research trend was observed between 2006 and 2020, with increased attention on research themes related to ‘risk assessment and adaptation’, ‘sustainable development’, ‘extreme events’ and ‘air pollution’, and reduced attention on research themes related to ‘ocean’, ‘infectious disease’ and ‘phenology’. The shift of the research trend was mainly driven by research in developed countries. Suggestions, recommendations and future priorities identified by experts in the field of atmospheric sciences, epidemiology, public health, climate change, environmental sciences, and policy development are also provided to guide future research. It is important to shift our focus from single health aspects to an integrated system (such as One Health framework, which considers environmental health, animal health and human health as a whole), with future research focusing more on the systemic impact of climate change in order to achieve better, more effective and efficient risk governance. More funding should be mobilized to support the research capacity building in developing countries and to support climate change adaptation strategies for sustainable development.

A social network analysis of interlocking directorates in the accommodation sector

PurposeBy adopting network analytic techniques, this paper aims to examine interlocking directorates among firms operating in the hospitality services sector in seven major Italian tourism destinations.Design/methodology/approachThe authors collected information for all the hotel corporations whose headquarters are located in the seven top Italian destinations: Florence, Milan, Naples, Rimini, Rome, Turin and Venice. Data come from the Analisi Informatizzata delle Aziende Italiane database by Bureau Van Dijk and were used to build a network where the nodes are board members (people) and corporations (hotels) and the links represent the membership of individuals in the boards. From this, with a one-mode projection, the authors obtain two networks: people and corporations. The overall networks’ structures are analysed by assessing their connectivity characteristics.FindingsThe findings indicate a relatively low number of interlocks that signals a high degree of fragmentation, showing that the interconnections (both within and between destinations) are scarce. This suggests that in absence of formalized cooperation arrangements, corporations might collaborate informally.Research limitations/implicationsThis work extends previous research on complexity in business settings, focusing specifically on service companies whose output depends on multiple interactions and helps clarifying coopetition practices of hospitality service firms. Policymaking perspectives are discussed as well as managerial viewpoints.Originality/valueNot many studies of the interlocking directorates in the hospitality domain exist. This paper uses network analysis for a better understanding of the cooperative practices and the formal social structures of the Italian hospitality industry and derives a series of implications important for both researchers and practitioners while also looking at potential future studies.

토픽모델링 분석을 이용한 학습역량 연구 동향 분석 및 대학 학습지원 방향 모색

Objectives Universities have expanded various support to strengthen students' learning capabilities. However, the meaning and interpretation of learning capabilities were diverse and ambiguous, causing uncertainty in deriving directions for setting learning support goals and establishing promotion strategies. Recognizing these problems, this study aims to prepare evidence for establishing a university learning support system by analyzing trends in studies related to learning competency and extracting topics related to learning competency through text mining and network analysis.
 Methods Basic statistical analysis, core word selection, topic extraction, and research trend were analyzed using keyword network analysis and LDA-based topic modeling techniques by collecting information on the titles and abstracts of 621 academic papers on learning competencies.
 Results The keywords related to learning competency were groups, and the results of selecting key words were learning immersion, self-efficacy, self-regulated learning ability, learning community, problem-solving ability, cooperative learning, knowledge, and creativity. As a result of topic modeling, five topics were extracted, and they were named as learning motivation, learning analysis, learning execution, learning relationship, and learning expansion. Next, as a result of analysis of research trends related to learning competency, it was found that research on learning competency has continued to increase since 2010, and research on learning relationships and learning expansion topics has increased since 2015.
 Conclusions This study is meaningful in that it identified the keywords mentioned in current learning-related studies to foster future talent, extracted and presented related topics, and presented basic data on finding effective support directions to improve student learning competency in universities.

Empowerment of women's entrepreneurship in family business through Twitter

PurposeThe main purpose is to provide a global understanding of the role of women in entrepreneurship and family businesses, enabling the evaluation of the impact and the sentiment their role generates. To this end, empowerment and businesswomen's positioning through user-generated content (UCG) on Twitter is assessed.Design/methodology/approachThe research is carried out from a quantitative and qualitative perspective through the evaluation of UGC from the social platform Twitter. A total of 37,852 tweets have been collected and subsequently analysed about the role of entrepreneurial women. For that purpose, a set of supervised machine learning algorithms have been developed for sentiment analysis, as a natural language processing (NLP) technique, outlining random forest as the one with the highest accuracy. Finally, social network analysis (SNA) techniques and graph theory are applied to a generated text-to-network, which enables the identification of the most relevant topics in the discussion.FindingsThe results revealed a positive relationship in the sentiment of the generated content in relation to women entrepreneurs and leaders. An increasing trend was evidenced in the number of published tweets, as well as in the identified topics, highlighting the needs and challenges faced by women in the business environment as the most widely discussed.Research limitations/implicationsThe study develops both theoretical and practical implications so that the findings result in applications in academia and society. The performed analysis creates consciousness about the challenges of women in society, specifically in entrepreneurship.Originality/valueThe study contributes to further enriching the literature on women's entrepreneurship by addressing UGC via Twitter around the role of women, entrepreneurship and power positions.

Promote Community Engagement in Participatory Research for Improving Breast Cancer Prevention: The P.I.N.K. Study Framework

Breast cancer (BC) has overtaken lung cancer as the most common cancer in the world and the projected incidence rates show a further increase. Early detection through population screening remains the cornerstone of BC control, but a progressive change from early diagnosis only-based to a personalized preventive and risk-reducing approach is widely debated. Risk-stratification models, which also include personal lifestyle risk factors, are under evaluation, although the documentation burden to gather population-based data is relevant and traditional data collection methods show some limitations. This paper provides the preliminary results from the analysis of clinical data provided by radiologists and lifestyle data collected using self-administered questionnaires from 5601 post-menopausal women. The weight of the combinations of women's personal features and lifestyle habits on the BC risk were estimated by combining a model-driven and a data-driven approach to analysis. The weight of each factor on cancer occurrence was assessed using a logistic model. Additionally, communities of women sharing common features were identified and combined in risk profiles using social network analysis techniques. Our results suggest that preventive programs focused on increasing physical activity should be widely promoted, in particular among the oldest women. Additionally, current findings suggest that pregnancy, breast-feeding, salt limitation, and oral contraception use could have different effects on cancer risk, based on the overall woman's risk profile. To overcome the limitations of our data, this work also introduces a mobile health tool, the Dress-PINK, designed to collect real patients' data in an innovative way for improving women's response rate, data accuracy, and completeness as well as the timeliness of data availability. Finally, the tool provides tailored prevention messages to promote critical consciousness, critical thinking, and increased health literacy among the general population.

Characterizing equitable access to grocery stores during disasters using location-based data

Natural hazards cause disruptions in access to critical facilities, such as grocery stores, impeding residents’ ability to prepare for and cope with hardships during the disaster and recovery; however, disrupted access to critical facilities is not equal for all residents of a community. In this study, we examine disparate access to grocery stores in the context of the 2017 Hurricane Harvey in Harris County, Texas. We utilized high-resolution location-based datasets in implementing spatial network analysis and dynamic clustering techniques to uncover the overall disparate access to grocery stores for socially vulnerable populations during different phases of the disaster. Three access indicators are examined using network-centric measures: number of unique stores visited, average trip time to stores, and average distance to stores. These access indicators help us capture three dimensions of access: redundancy, rapidity, and proximity. The findings show the insufficiency of focusing merely on the distributional factors, such as location in a food desert and number of facilities, to capture the disparities in access, especially during the preparation and impact/short-term recovery periods. Furthermore, the characterization of access by considering combinations of access indicators reveals that flooding disproportionally affects socially vulnerable populations. High-income areas have better access during the preparation period as they are able to visit a greater number of stores and commute farther distances to obtain supplies. The conclusions of this study have important implications for urban development (facility distribution), emergency management, and resource allocation by identifying areas most vulnerable to disproportionate access impacts using more equity-focused and data-driven approaches.

Modelling the energy conservation behaviour among Chinese households under the premises of value-belief-norm theory

Energy conservation is a necessary form of green behaviour, as energy production and consumption gravely affect the climate. The current study aimed to explore energy conservation behaviour among Chinese households based on the value-belief-norm framework. This study used a cross-sectional design and collected quantitative data from 1671 respondents through an online survey. The hybrid analysis techniques of partial least squares structural equation modelling and artificial neural network analysis were used to analyse the data. Findings revealed that biospheric values have a positive and significant effect on pro-environmental beliefs, awareness of consequences, and ascription of responsibility, which ultimately explains 46.3% of the change in personal norms and 42.6% of the change in green trust. The results shed light on the significant positive impact of green trust and personal norms on the energy conservation intention. Green trust and personal norms can elucidate 27.3% of the change in energy conservation intention. The energy conservation intention (39.1%) explains the energy conservation behaviour. The results of ANN analysis revealed energy conservation intention, personal norms, green trust, and awareness of consequences as the four most significant contributors to the formation of energy conservation behaviour. The current study extended the VNB model with the green trust. It offered empirical evidence on the effects of pro-environmental belief, awareness of consequences, and ascription of responsibility concerning energy conservation intention. Energy policies should thus concentrate on addressing energy conservation behaviour, promoting energy-efficient household appliances, and rewarding energy conservation by lowering energy prices for low-energy users.

Discovery and classification of complex multimorbidity patterns: unravelling chronicity networks and their social profiles

Multimorbidity can be defined as the presence of two or more chronic diseases in an individual. This condition is associated with reduced quality of life, increased disability, greater functional impairment, increased health care utilisation, greater fragmentation of care and complexity of treatment, and increased mortality. Thus, understanding its epidemiology and inherent complexity is essential to improve the quality of life of patients and to reduce the costs associated with multi-pathology. In this paper, using data from the European Health Survey, we explore the application of Mixed Graphical Models and its combination with social network analysis techniques for the discovery and classification of complex multimorbidity patterns. The results obtained show the usefulness and versatility of this approach for the study of multimorbidity based on the use of graphs, which offer the researcher a holistic view of the relational structure of data with variables of different types and high dimensionality.

The prediction of mid-winter and spring breakups of ice cover on Canadian rivers using a hybrid ontology-based and machine learning model

Mid-winter breakups (MWBs) are an increasingly common event on Canadian rivers resulting in the early breakup of river ice cover, with complex and numerous drivers. This study focuses on the development of an MWB Ontology which allows the key data, events, and relationships in an ice season to be defined and analyzed. The MWB Ontology is applied to a national case study of 54 rivers in Canada. Through assessment of the MWB Ontology with network analysis techniques, a hybrid modelling framework coupling the MWB Ontology with machine learning is developed. The hybrid models produce greatly reduced errors in their forecasts of the timing breakup events, with the best performance being a mean absolute error of 12.47 days for MWBs and 10.68 days for spring breakup. The results demonstrate the utility of the MWB Ontology as a tool for collating data and a means for analysis and forecasting of these events.

Analisis Jaringan Komunikasi German Digital #PERCUMALAPORPOLISI di Twitter

In the early of October 2021, the Indonesian people were busy discussing the performance of the Indonesian National Police (POLRI) through the hashtag #percumalaporpolisi on Twitter social media. The hashtag is a form of digital movement from Twitter netizens about the lack of performance of the POLRI. Users interact with each other through these hashtags to form a communication network. This study analyzes quantitatively the communication network formed through #percumalaporpolisi on Twitter. This study uses communication network theory by Barry Wellman and analyzed using communication network analysis techniques based on Big Data. This hashtag was analyzed using communication network analysis techniques through three levels, namely network level, behavior level, and content level. The devices used in this research are Gephi and Rstudio applications. This study aimed to see how the networks were formed and how the analysis is on each level. Keywords: Big Data, Twitter, Communication Network Analysis, Gephi

Structural Cohesion, Role Equivalence, or Homophily: Which Process Best Explains Social Homogeneity?

Social homogeneity, understood as the similarity of perceptions and attitudes that individuals display toward the environment around them, is explained by the relational context in which they are immersed. However, there is no consensus about which relational mechanism best explains social homogeneity. The purpose of this research is to find out which of the three classical relational processes most studied in network analysis (structural cohesion, role equivalence, or homophily) is more determinant in explaining social homogeneity. To achieve the research objective, 110 professionals (psychologists, social workers, and community facilitators) implementing a psychosocial care program in three regions of Northwest Colombia were interviewed. Different types of relationships among professionals were analyzed using network analysis techniques. To examine the structural cohesion hypothesis, interveners were categorized according to the level of structural cohesion by performing core-periphery analysis in the networks evaluated; to test the role equivalence hypothesis, participants were categorized according to their level of degree centrality in the networks examined; to test the homophily hypothesis, participants were grouped according to the level of homophily in terms of professional profile. The non-parametric tests showed that role equivalence was the most powerful mechanism for explaining social homogeneity in the sample of psychosocial interveners evaluated.

Workplace pro-environmental behaviour: a review and bibliometric analysis

PurposePro-environmental behaviour (PEB) has always been one of the most critical topics in academia and industry. The primary goal of this study is to identify the current research progress and developmental trends of PEB by doing an exhaustive analysis of the existing literature. The study also aims to streamline the future research possibilities in PEB with the current research interests.Design/methodology/approachA total of 209 published articles from 1993 to 2021 were collected from the Scopus database and analysed using an integrated approach of bibliometric analysis, network analysis, and content analysis techniques. The bibliometric analysis was performed with the help of statistical program “R,” and further analysis was carried out using Gephi and VOS viewer software.FindingsThe systematic mapping of this field gives an exhaustive review of the available literature by identifying annual publication growth, most contributing authors, journals, emerging keywords, and themes. Sustainability was found to be the most contributing journal, and Paille was the topmost contributing author. The keywords such as corporate social responsibility, organizational citizenship behaviour towards the environment (OCBE), and green human resource management (HRM) were recently identified as the emerging niche in the literature. The content analysis findings give a robust roadmap for further research in this area.Originality/valueThis study enriches the literature on workplace pro-environmental behaviour by presenting a detailed, systematic, and exhaustive review of the existing publications using a combination of research methodology.

텍스트 마이닝 분석을 이용한 아파트 광고 메시지 연구

This study conducted an analysis with the semantic network analysis technique to figure out house values sought after in the messages of local apartment sales advertisements. The study collected analysis data from the messages of 54 apartment sales advertisements in total published in B, which is one of the most representative newspapers in Busan and has the most subscribers, for two years of June 2020~June 2022. The analysis focus was put on the headlines, subheadlines, body copies, and captions of newspaper ads. R was used in a text mining analysis including the analysis of word clouds and semantic networks as well as frequency analysis to identify the housing value factors conveyed to consumers exposed to these advertising messages. The main findings were summarized as follows: first, such keywords as

Network analysis of international export pattern.

There is a relationship between the trade flows of the countries and their economic growth potential and development. One way to analyze the trade flows of countries with each other is the use of network analysis techniques. Network analysis uses a visual, mathematical and statistical approach to identify and quantify the structural properties of networks. In this study, the relationship of the first 50 countries in the world in terms of export volume was evaluated with Gephi 0.9.2, one of the social network analysis programs. The effect of the COVID-19 epidemic disease on the commercial flows of the countries is the main purpose of the study. The existence of the effective role of geographic proximity in commercial flows, the commercial partners of the countries, the centrality criteria and the existence of countries that have an active role in the world are clarified with this study.

Identifying Hub Genes and Immune Cell Infiltration for the Progression of Carotid Atherosclerotic Plaques in the Context of Predictive and Preventive Using Integrative Bioinformatics Approaches and Machine-Learning Strategies.

Emerging evidence shows that carotid atherosclerosis is related to the activation of immune-related pathways and inflammatory cell infiltration. However, the immune-linked pathways that helped in the advancement of the carotid atherosclerotic plaque and the association of such plaques with the infiltration status of the body's immune cells still unclear. Here, the expression profiles of the genes expressed during the progression of the carotid atherosclerotic plaques were retrieved from the Gene Expression Omnibus database and 178 differentially expressed genes were examined. The Weighted Gene Coexpression Network Analysis technique identified one of the brown modules showed the greatest correlation with carotid atherosclerotic plaques. In total, 66 intersecting genes could be detected after combining the DEGs. LASSO regression analysis was subsequently performed to obtain five hub genes as potential biomarkers for carotid atherosclerotic plaques. The functional analysis emphasized the vital roles played by the inflammation- and immune system-related pathways in this disease. The immune cell infiltration results highlighted the significant correlation among the CD4+ T cells, B cells, macrophages, and CD8+ T cells. Thereafter, the gene expression levels and the diagnostic values related to every hub gene were further validated. The above results indicated that macrophages, B cells, CD4+ T cells, and CD8 + T cells were closely related to the formation of the advanced-stage carotid atherosclerotic plaques. Based on the results, it could be hypothesized that the expression of hub genes (C3AR1, SLAMF8, TMEM176A, FERMT3, and GIMAP4) assisted in the advancement of the early-stage to advanced-stage carotid atherosclerotic plaque through immune-related signaling pathways. This may help to provide novel strategies for the treatment of carotid plaque in the context of predictive, preventive, and personalized medicine.

Linked in the dark: A network approach to understanding information flows within the Dutch Telegramsphere

ABSTRACT Recent studies have shown that the stricter content moderation policies imposed by mainstream social networking sites (SNSs) stimulated the growth of low-moderated but relatively open discussion platforms such as Telegram. Despite Telegram’s growing popularity among (deplatformed) digital exiles, and high potential for news dissemination, information consumption, mobilization, and radicalization, little is known about information flows with respect to politically and socially relevant topics within the Telegramsphere. We scrutinize the Telegramsphere as an information-sharing ecosystem of current affairs by uncovering how information flows indicated by content-overlap and shared users influenced the structure of Telegram networks and shaped communities over time. Using state-of-the-art web-mining, neural topic modeling, and social network analysis techniques on a unique data set that spans the full messaging history (N = 2,033,661) of 174 Dutch-language public Telegram chats/channels, we show that over time, conspiracy-themed, far-right activist, and COVID-19-sceptical communities dominated the Dutch Telegramsphere of current affairs. Our findings raise concerns with respect to Telegram’s polarization and radicalization capacity in the context of consuming socially and politically relevant information online.

Ultra-broadband FeNi3/NiZnFe2O4/ZnO composite powders for microwave absorption

FeNi3/NiZnFe2O4/ZnO composite powders were in-situ prepared for microwave absorption by solution combustion synthesis method in a closed system. The effects of fuel content and composition on the structural, microstructural, and magnetic and microwave absorption properties were studied by X-ray diffractometry, scanning electron microscopy, vibrating sample magnetometry, and vector network analyzer techniques. There were FeNi3, NiZnFe2O4 and ZnO phases in the samples obtained at higher fuel contents and higher amounts of Zn2+ substitution. The synthesized powders had porous and spongy microstructure in which the porosity and particle size decreased with the fuel contents. The values of permittivity and permeability decreased with Zn2+ addition. By controlling over the amounts of fuel and substituted Zn2+ species, the values of saturation magnetization and the coercivity were tuned in the range of 30–70 emu/g and 153–131 Oe, respectively. The composite sample containing NiZnFe2O4-34 wt.% FeNi3-8 wt. % ZnO exhibited a minimum reflection loss of −40 dB and an effective absorption bandwidth of 4.6 GHz in Ku band at the matching thickness of 1.5 mm. The NiZnFe2O4-17 wt.% FeNi3-32 wt. % ZnO composite powders had the broader effective absorption bandwidth of 6.7 GHz at the matching thickness of 2.3 mm, but with the minimum reflection loss of −24 dB at a matching frequency of 16.5 GHz.

Research topic flows in co-authorship networks

AbstractIn scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that Topic Flow Networks are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.

Untying a Gordian knot: Exploring the nomological network of resilience.

This study investigates the relations of resilience with coping, personality traits, emotional intelligence, sense of coherence and maladaptive personality traits. The study employs network analysis techniques to the study of resilience, showcasing how these methods can estimate a model that is simple to interpret while still retaining the most important relations and that can even suggest the direction of causality despite using a cross-sectional design (N=305). The results highlight several important variables that should be considered for fostering resilience, foremost among them the use of positive reappraisal coping, sense of coherence, and the social management aspect of emotional intelligence. The results successfully replicated known associations between resilience and other psychological constructs (emotional intelligence, personality, sense of coherence, coping) and shed light on relations between resilience and maladaptive personality traits. Network analysis considered all these constructs together, so as to take into account the complex pattern of relations between them and offer a bird's eye view of the whole network of associations centred on resilience. The resulting model is parsimonious and easy to interpret while still striving to preserve the complexity of the variables' interrelations.

What Do We Know about Crowdfunding and P2P Lending Research? A Bibliometric Review and Meta-Analysis

In the era of fintech, businesses using technology other than traditional banks are providing financial services. Crowdfunding and peer-to-peer (P2P) lending are two of the most exciting financial innovations of the twenty-first century. In this paper, we use a bibliometric review and meta-analysis to understand the academic research on crowdfunding and P2P lending. Our findings show that the research on this topic has grown a lot in terms of publications since 2013 and the maximum mean total citations were observed in the year 2014. We provide the details about the most influential authors based on total citations, authors with the greatest number of publications, the most influential documents, significant journal sources, highest single country production, multiple country production, and important affiliations. We further apply the network analysis and visualisation techniques wherein we provide the details of the citation analysis of documents, co-citation analysis of authors, and co-occurrence analysis of author keywords. Finally, we provide the future directions of the research on this burgeoning topic.