Combination Of Network Analysis Research Articles

Identifying interdisciplinary emergence in the science of science: combination of network analysis and BERTopic

Global scientific output is expanding exponentially, which in turn calls for a better understanding of the science of science and especially how the boundaries of scientific fields expand through processes of emergence. The present study proposes the application of embedded topic modeling techniques to identify new emerging science via knowledge recombination activities as evidenced through the analysis of research publication metadata. First, a dataset is constructed from metadata derived from the Web of Science Core Collection database. The dataset is then used to generate a global map representing a categorical scientific co-occurrence network. A research field is defined as interdisciplinary when multiple science categories are listed in its description. Second, the co-occurrence networks are subsequently compared between periods to determine changing patterns of influence in light of interdisciplinarity. Third, embedded topic modeling enables unsupervised association of interdisciplinary classification. We present the results of the analysis to demonstrate the emergence of global interdisciplinary sciences and further we perform qualitative validation on the results to identify what the sources of the emergent areas are. Based on these results, we discuss potential applications for identifying emergence through the merging of global interdisciplinary domains.

NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning

Network node embedding captures structural and relational information of nodes in the network and allows for us to use machine learning algorithms for various prediction tasks on network data that have an inherently complex and disordered structure. Network node embedding should preserve as much information as possible about important network properties where information is stored, such as network structure and node properties, while representing nodes as numerical vectors in a lower-dimensional space than the original higher dimensional space. Superior node embedding algorithms are a powerful tool for machine learning with effective and efficient node representation. Recent research in representation learning has led to significant advances in automating features through unsupervised learning, inspired by advances in natural language processing. Here, we seek to improve the representation quality of node embeddings with a new node vectorization technique that uses network analysis to overcome network-based information loss. In this study, we introduce the NodeVector algorithm, which combines network analysis and neural networks to transfer information from the target network to node embedding. As a proof of concept, our experiments performed on different categories of network datasets showed that our method achieves better results than its competitors for target networks. This is the first study to produce node representation by unsupervised learning using the combination of network analysis and neural networks to consider network data structure. Based on experimental results, the use of network analysis, complex initial node representation, balanced negative sampling, and neural networks has a positive effect on the representation quality of network node embedding.

Sustainability of the local stakeholder network in semi-steppe rangelands in southern Iran

Sustainable land management (SLM) as a holistic method seeks to combine ecological values with socio-cultural and policy concepts, through which local populations play a significant role in achieving this management. Qasr Yaqoub village was selected for the present study, which is located in Safashahr city in Fars province, south of Iran. The common method of herding in Qasr Yaqoub village is participatory and in the form of assistance systems. Therefore, in order to have a comprehensive evaluation of the stability of the local stakeholder network in different dimensions of sustainability and social networks, two methods of indicators analysis including social network analysis (SNA) and sustainability barometer model (SBM) were used. The index values for trust and social cooperation ties among stakeholders in the local stakeholders' network in Qasr Yaqoub village, consisting of 42 individuals, were found to be 63.1% and 59.8%, respectively. These values indicate a stable density in the trust network and an average density in the cooperation network among stakeholders in the region. In addition, the social network index, which measures the extent of connections, is 1086 for trust and 1030 for social cooperation, both covering more than 50% of the expected ties. This signifies a significant level of interaction between stakeholders regarding relevant issues, facilitating the promotion of social cohesion in the study area. These findings highlight the positive dynamics within the stakeholder network, fostering trust, cooperation, and social cohesion in Qasr Yaqoub village. This study showed that the combination of network analysis and sustainability barometer model could be a useful tool in the sustainability analysis of local communities in rangeland ecosystems.

What facilitates or prevents academic fraud in a Colombian faculty of medicine-Protocol of a study using fuzzy cognitive mapping.

Academic fraud is any behavior that gives a student an undeserved advantage over another student. Few studies have explored the causes of and possible solutions to academic fraud in Latin America. We aim to map the knowledge of stakeholders in a Colombian faculty of medicine about the factors that facilitate and prevent academic fraud. Fuzzy cognitive mapping. We will use the approach proposed by Andersson and Silver to generate fuzzy cognitive maps representing stakeholder knowledge. This process consists of ten steps: (1) definition of the research question; (2) identification of participants; (3) generation of ideas; (4) rationalization of ideas; (5) organization and connection of ideas; (6) weighing; (7) pattern grouping; (8) list of links and digitization; (9) combination of maps and network analysis; and (10) deliberative dialogue. To draw the maps, we will invite medical students, interns, resident physicians, master's students, and professors in the faculty of medicine. Four medical students will receive training to facilitate the sessions. Participants will identify the factors contributing to academic fraud and their causal relationships. We will use a combination of network analysis and graph theory to identify the chains of factors with greatest influence on academic fraud. The maps will serve to discuss strategies to reduce academic fraud in the Faculty of Medicine and to identify factors that could be addressed in other contexts with similar problems. This research will allow the students who facilitate mapping sessions to learn about research techniques, fuzzy cognitive mapping and academic fraud. Study registration: Registered in OSF Registries on August 2nd, 2022. Registration number: osf.io/v4amz.

Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19.

The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic. However, GT's normalization of the search volumes data and data retrieval restrictions affect the data resolution in reflecting the actual search behaviors, thus limiting the potential for using GT data to predict disease outbreaks. This study aimed to introduce a merged algorithm that helps recover the resolution and accuracy of the search volume data extracted from GT over long observation periods. In addition, this study also aimed to demonstrate the extended application of merged search volumes (MSVs) in combination of network analysis, via tracking the COVID-19 pandemic risk. We collected relative search volumes from GT and transformed them into MSVs using our proposed merged algorithm. The MSVs of the selected coronavirus-related keywords were compiled using the rolling window method. The correlations between the MSVs were calculated to form a dynamic network. The network statistics, including network density and the global clustering coefficients between the MSVs, were also calculated. Our research findings suggested that although GT restricts the search data retrieval into weekly data points over a long period, our proposed approach could recover the daily search volume over the same investigation period to facilitate subsequent research analyses. In addition, the dynamic time warping diagrams show that the dynamic networks were capable of predicting the COVID-19 pandemic trends, in terms of the number of COVID-19 confirmed cases and severity risk scores. The innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful for predicting the pandemic risk. Further investigation of the GT dynamic network can focus on noncommunicable diseases, health-related behaviors, and misinformation on the internet.

The Role of Intermediate Collaborative Forums in Polycentric Environmental Governance

Abstract In complex, polycentric environmental governance systems, actors may choose to collaborate with one another to reduce their collective vulnerability and enhance system function. However, collaboration can be costly, and little evidence exists for how particular collaborative forums impact the broader governance system in which they are embedded. To address this gap, we investigate the role of intermediate collaborative forums, which support collaboration among a subset of system actors, in polycentric governance systems. Empirically, we analyze the structural and functional role of an intermediate collaborative forum called the Arizona Municipal Water Users Association (AMWUA) within the municipal surface water governance network for the Phoenix Metropolitan Area (PMA) in Arizona, United States. To do this, we draw from 21 interviews with water professionals in the PMA, which we analyze through a combination of network analysis and qualitative coding. We find that AMWUA facilitates strong bonding capacities among members, allowing for streamlined bridging to the rest of the network that enhances information processing and advocacy of member needs. Our findings advance theory on the role of collaboration in polycentric systems and inform the design of collaborative institutions to improve environmental governance.

Biofilm: A strategy for the dominance of comammox Nitrospira

The discovery of a complete ammonia oxidizer (comammox Nitrospira) challenged the traditional theory of two step nitrification. Comammox Nitrospira co-occurred and competed with traditional ammonia oxidizers in many habitats. However, the ubiquitous existence and high abundance of comammox Nitrospira in drinking-water treatment plants (DWTPs) suggested that this habitat could lead to niche partitioning among ammonia oxidizers. Thus, revealing the potential mechanisms could explain where and how comammox Nitrospira dominated ammonia oxidation. In this study, investigations of in situ DWTPs and ex situ bioreactors were combined to reveal the mechanisms. Results indicated that comammox Nitrospira was the most abundant (79%–97%) and most active (66%–86%) ammonia oxidizer in attached growth tanks of DWTPs, which was 1.3–1.7 times higher than that in the suspended growth system. Furthermore, to confirm whether attached growth was the key to the dominance of comammox Nitrospira, two bioreactors, with quartz sand and hollow sphere as fillers (fillers used in DWTPs), were operated for 390 days. Results showed that comammox Nitrospira was the primary contributor (52.5%–90.4%) to ammonia oxidation in both reactors. The combination of network analysis and genome bins showed that comammox Nitrospira could create a two-way physiological feeding by exchanging self-produced cobalamin with amino acids from co-occurrent partners. Thus, comammox Nitrospira would dominate ammonia oxidation in an attached growth system. Based on its greater adaptability to low substrate concentrations, comammox Nitrospira may be the core of a new bioprocess and play an important role in drinking water treatment.

Causality Between International Trade and International Patenting: A Combination of Network Analysis and Granger Causality

Causality Between International Trade and International Patenting: A Combination of Network Analysis and Granger Causality

Evolving industrial districts and changing innovation patterns: the case of Montreal

PurposeThis paper aims to investigate the evolution of the phenomenon of industrial districts and explores the broader regional innovation systems that consist of multiple industrial districts.Design/methodology/approachThis paper uses a combination of network analysis and patent analysis techniques to analyze the social structure of Montreal tech agglomeration and its innovation.FindingsThe findings indicate that the cores of modern regional innovation systems are composed of densely collaborating organizations belonging to different industrial clusters, and these organizations are responsible for the most radical innovations. The analysis also reveals the importance of brokers and international ties in generating radical innovations.Research limitations/implicationsThe findings of our paper extend the initial concept of industrial district and call for the need to no longer focus exclusively on individual clusters, but to take into consideration broader competitive regional innovation systems that are composed of multiple clusters. The current trend of the core of such systems to be composed of organizations from multiple clusters indicates that the traditional understanding of industrial district confined to the borders of specific industry is no longer relevant and there is a need to revise the conceptualization of clusters and further analyze the social fabric of broader regional innovation systems. In future, such intense collaboration within the core of the regional innovation system network may give rise to new industrial and technological configurations. It is important to further investigate these structures, because they have important implications for innovation and are responsible for new innovation patterns.Practical implicationsTo boost innovation in specific localities, policymakers could encourage collaboration between different clusters and support interdisciplinary projects and programs. Those would help the local community generate radical innovations.Social implicationsUsing this research, local policymakers could help local companies understand and explore international markets, as well as focus on attracting multinational firms that are leaders in their respective fields. Finally, local policymakers could further support important cluster intermediariesOriginality/valueThis paper offers original contributions to the studies of industrial districts as it explores a competitive ecosystem composed of multiple industrial districts and analyzes how these industrial districts interact and where the most innovative solutions lie in the social fabric of this big ecosystem.

Analyzing protest mobilization on Telegram: The case of 2019 Anti-Extradition Bill movement in Hong Kong.

Online messaging app Telegram has increased in popularity in recent years surpassing Twitter and Snapchat by the number of active monthly users in late 2020. The messenger has also been crucial to protest movements in several countries in 2019-2020, including Belarus, Russia and Hong Kong. Yet, to date only few studies examined online activities on Telegram and none have analyzed the platform with regard to the protest mobilization. In the present study, we address the existing gap by examining Telegram-based activities related to the 2019 protests in Hong Kong. With this paper we aim to provide an example of methodological tools that can be used to study protest mobilization and coordination on Telegram. We also contribute to the research on computational text analysis in Cantonese—one of the low-resource Asian languages,—as well as to the scholarship on Hong Kong protests and research on social media-based protest mobilization in general. For that, we rely on the data collected through Telegram’s API and a combination of network analysis and computational text analysis. We find that the Telegram-based network was cohesive ensuring efficient spread of protest-related information. Content spread through Telegram predominantly concerned discussions of future actions and protest-related on-site information (i.e., police presence in certain areas). We find that the Telegram network was dominated by different actors each month of the observation suggesting the absence of one single leader. Further, traditional protest leaders—those prominent during the 2014 Umbrella Movement,—such as media and civic organisations were less prominent in the network than local communities. Finally, we observe a cooldown in the level of Telegram activity after the enactment of the harsh National Security Law in July 2020. Further investigation is necessary to assess the persistence of this effect in a long-term perspective.

New Insights into the Co-Occurrences of Glycoside Hydrolase Genes among Prokaryotic Genomes through Network Analysis.

Glycoside hydrolase (GH) represents a crucial category of enzymes for carbohydrate utilization in most organisms. A series of glycoside hydrolase families (GHFs) have been classified, with relevant information deposited in the CAZy database. Statistical analysis indicated that most GHFs (134 out of 154) were prone to exist in bacteria rather than archaea, in terms of both occurrence frequencies and average gene numbers. Co-occurrence analysis suggested the existence of strong or moderate-strong correlations among 63 GHFs. A combination of network analysis by Gephi and functional classification among these GHFs demonstrated the presence of 12 functional categories (from group A to L), with which the corresponding microbial collections were subsequently labeled, respectively. Interestingly, a progressive enrichment of particular GHFs was found among several types of microbes, and type-L as well as type-E microbes were deemed as functional intensified species which formed during the microbial evolution process toward efficient decomposition of lignocellulose as well as pectin, respectively. Overall, integrating network analysis and enzymatic functional classification, we were able to provide a new angle of view for GHs from known prokaryotic genomes, and thus this study is likely to guide the selection of GHs and microbes for efficient biomass utilization.

Assessment of Online Deliberative Quality: New Indicators Using Network Analysis and Time-Series Analysis

Online deliberation research has recently developed automated indicators to assess the deliberative quality of much user-generated online data. While most previous studies have developed indicators based on content analysis and network analysis, time-series data and associated methods have been studied less thoroughly. This article contributes to the literature by proposing indicators based on a combination of network analysis and time-series analysis, arguing that it will help monitor how online deliberation evolves. Based on Habermasian deliberative criteria, we develop six throughput indicators and demonstrate their applications in the OmaStadi participatory budgeting project in Helsinki, Finland. The study results show that these indicators consist of intuitive figures and visualizations that will facilitate collective intelligence on ongoing processes and ways to solve problems promptly.

Policy Debates and Discourse Network Analysis: A Research Agenda

Discourse network analysis (DNA) is a combination of network analysis and qualitative content analysis. DNA has been applied to various policy processes and debates to show how policy actors are related at the discursive level, complementing coordination relations among them that are often analysed in the application of the policy networks approach. This editorial takes stock of the theoretical and methodological research frontiers in DNA and summarises the contributions of the eleven articles in the thematic issue on “Policy Debates and Discourse Network Analysis” in <em>Politics and Governance</em>.

Host Phylogeny, Geographic Overlap, and Roost Sharing Shape Parasite Communities in European Bats

How multitrophic relationships between wildlife communities and their ectoparasitic vectors interact to shape the diversity of vector-borne microorganisms is poorly understood. Nested levels of dependence among microbes, vectors, and vertebrate hosts may have complicated effects on both microbial community assembly and evolution. We examined Bartonella sequences from European bats and their ectoparasites with a combination of network analysis, Bayesian phylogenetics, tip-association and cophylogeny tests, and linear regression to understand the ecological and evolutionary processes that shape parasite communities. We detected seven bat-ectoparasite-Bartonella communities that can be differentiated based on bat families and roosting patterns. Tips of the Bartonella tree were significantly clustered by host taxonomy and geography. We also found significant evidence of evolutionary congruence between bat host and Bartonella phylogenies, indicating that bacterial species have evolved to infect related bat species. Exploring these ecological and evolutionary associations further, we found that sharing of Bartonella species among bat hosts was strongly associated with host phylogenetic distance and roost sharing and less strongly with geographic range overlap. Ectoparasite sharing between hosts was strongly predicted by host phylogenetic distance, roost sharing, and geographic overlap but had no additive effect on Bartonella sharing. Finally, historical Bartonella host-switching was more frequent for closely related bats after accounting for sampling bias among bat species. This study helps to disentangle the complex ecology and evolution of Bartonella bacteria in bat species and their arthropod vectors. Our work provides insight into the important mechanisms that partition parasite communities among hosts, particularly the effect of host phylogeny and roost sharing, and could help to elucidate the evolutionary patterns of other diverse vector-borne microorganisms.

Fine-Grained Subjective Partitioning of Urban Space Using Human Interactions From Social Media Data

Fine-grained subjective partitioning of urban space using human activity flows reveals actual human activity spaces with high resolution, which has great implications for the development and validation of planning strategies. This paper presents a new method for fine-grained subjective partitioning of urban space based on the combination of network analysis and human interactions from social media. Three main procedures are involved in this method: 1) a cut-off point for hierarchical partitioning is determined by fitting the probability distribution function of human activity patterns; 2) based on this cut-off point, improved hierarchical weighted-directed spatial networks are constructed by incorporating a gravity model into conventional spatial networks to take into account the importance of the attraction of nodes in shaping urban space; and 3) the hierarchical and fine-grained partitioning results, which reveal the actual human activity spaces with high resolution at multiscale are obtained by implementing a spatial community detection algorithm in these networks. A case study, using a real-world dataset from the capital of China validates the effectiveness of the proposed method. By analyzing the results from Beijing, we concluded that the social media, a gravity model, and the hierarchical subjective communities detected from the hierarchical human activity networks are all outstanding contributors to the fine-grained subjective partitioning of urban spaces.

The influence of climatic legacies on the distribution of dryland biocrust communities.

Predicting the distribution of biocrust species, mosses, lichens and liverworts associated with surface soils is difficult, but climatic legacies (changes in climate over the last 20k years) can improve our prediction of the distribution of biocrust species. To provide empirical support for this hypothesis, we used a combination of network analyses and structural equation modelling to identify the role of climatic legacies in predicting the distribution of ecological clusters formed by species of mosses, lichens and liverworts using data from 282 large sites distributed across 0.6 million km2 of eastern Australia. Two ecological clusters contained 87% of the 120 moss, lichen and liverwort species. Both clusters contained lichen, moss and liverwort species, but were dominated by different families. Sites where the air temperature increased the most over 20kyears (positive temperature legacies) were associated with reductions in the relative abundance of species from the lichen (Peltulaceae and Teloschistaceae) and moss (Bryaceae) families (Cluster A species), greater groundstorey plant cover and lower soil pH. Sites where precipitation has increased over the past 20kyears (positive precipitation legacy) were associated with increases in the relative abundance of lichen (Cladoniaceae, Lecideaceae and Thelotremataceae) and moss (Pottiaceae) families (Cluster B species) and lower levels of soil pH. Sites where temperatures have increased the most in the past 20kyears suppressed the negative effects of plant cover on Cluster B by reducing plant cover. Increased intensity of grazing suppressed the negative effect of soil pH and the positive effect of soil carbon, on the relative abundance of Cluster B taxa. Finally, increasing temperature and precipitation legacies reduced the negative effect of soil pH on Cluster B. Understanding of the importance of climatic legacies improves our ability to predict how biocrust assemblies might respond to ongoing global environmental change associated with increasing land use intensification, increasing temperature and reduced rainfall.

Networks of Exchange Poetry in Late Medieval China: Notes toward a Dynamic History of Tang Literature

Abstract This article combines qualitative and quantitative methods to rethink the literary history of late medieval China (830–960 CE). It begins with an overview of exchange poetry in the Tang dynasty and its role in the construction of the poetic subject, namely, the poetic subject's distributed textual body. A total of 10,869 poems exchanged between 2,413 individuals are cataloged to seek the structure of the collectively imagined literary relations of the time. This catalog is subjected to social-network analysis to reveal patterns and peculiarities in the extant corpus of late medieval poetry, which in turn prompt close readings of the sources. These readings lead to four conclusions about the history of late medieval poetry: (a) Buddhist monks were hubs of literary activity, (b) the poet Jia Dao became an increasingly important site of connection over time, (c) the concept of “poetic schools” is not a useful lens through which to view the Late Tang, and (d) poets at the center of the network are increasingly characterized by their mobility. This combination of network analysis and close reading highlights the dynamic nature of Chinese literary history, providing insight into the ever-shifting conjunctures of forms, genres, expectations, and relations in the late medieval literary world.

Whose Story Wins on Twitter?

The South China Sea dispute is one of the most complicated geopolitical issues of the twenty-first century. While this international conflict revolves around military and economic disputes, in today’s information age international politics also hinges on how each country presents the dispute in the news and whose “story” wins. Based on the Network Agenda Setting model, this study analyzed the news coverage in three involved countries—China, the Philippines, and the United States—and examined whose “story” gains the most prominence on Twitter, an emerging transnational public sphere. A combination of network analysis and the Granger causality test were used to explicate the media effects. Network visualization techniques were adopted to graphically represent the media network agendas. Overall, the results showed that the Twittersphere to a great extent followed the US news media in reasoning the association and dynamics between different countries involved in the South China Sea dispute, whereas the media in China and the Philippines showed minimal to moderate impact on this global social media platform. The findings demonstrate that the US media’s agenda-setting effect can transcend national borders, and that the imbalanced power structure has been reinforced in this new media environment.

Standing and ‘survival’ in the adult film industry

ABSTRACTIt is conventional wisdom that knowing the right people is essential for one’s career. This claim is supported in the literature on social capital. However, the empirical evidence in this field remains ambiguous. While the literature recognizes that ‘connections’ help finding any job at all, it remains unclear if long-term benefits exist. In contrast to other industries, collaborations between performers in adult films are easily observed. Consequently, a collaborative network can be constructed which serves as an input in order to estimate the effect of a person’s centrality on individual success. Unfortunately, success is not easily observed either. Hence, in this manuscript, the survival in the industry is used as a proxy for professional success. This assumption is justified by the economic argument that, in the absence of lock-in effects, performers will remain in the industry as long as it remains profitable. The profitability does not only depend on monetary aspects but also includes costs from social stigma and adverse effects on health and mental well-being. Using a combination of network analysis and duration models, the results indicate that there is a strong correlation between network centrality and survival in the adult film industry.

Analyzing Organizational Networks Based on a Combination of Network Analysis with ABM Simulation

Analyzing Organizational Networks Based on a Combination of Network Analysis with ABM Simulation