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In this study, we analyze the changes over time in the complexity and structure of words used in article titles and the connections between articles in citation networks, focusing on the topic of artificial intelligence (AI) up to 2020. By measuring unpredictability in word usage and changes in the connections between articles, we gain insights into shifts in research focus and diversity of themes. Our investigation reveals correspondence between fluctuations in word complexity and changes in the structure of citation networks, highlighting links between thematic evolution and network dynamics. This approach not only enhances our understanding of scientific progress but also may help in anticipating emerging fields and fostering innovation, providing a quantitative lens for studying scientific domains beyond AI. [ABSTRACT FROM AUTHOR]

It is a common phenomenon for scientists to follow hot topics in research and this phenomenon can generally be quantified by measuring the preference attachment of new papers. A similar phenomenon also exists when a paper chooses its references. However, the abovementioned method does not apply to measure the preference for hot papers. To solve this problem, in this paper, we propose to convert measuring a paper's preference for hot papers into calculating the hotness obtained from a paper's references. We propose a PageRank-like algorithm that considers the hotness propagation based on citation relationships between papers to measure the hotness transfer of individual papers. We apply this method to the American Physical Society journals and explore the hotness transfer performance of individual papers in physics. It is found that highly innovative papers, such as Nobel Prize-winning papers in physics, have a weaker hotness transfer degree than papers with the same number of citations. We explore the factors associated with the performance of hotness transfer indicators. We find that the larger the size or citation counts of the field are, the stronger the hotness transfer degree of the field is likely to be. The team size and the number of references can also affect the hotness transfer degree of individual papers. Finally, we find that the hotness transfer scores of papers show an increasing trend over time. Relevant empirical discoveries may be valuable for evaluating paper impact. [ABSTRACT FROM AUTHOR]

This study investigates the coevolution of coauthorship and citation networks and their influence on scientific metrics such as the h-index and journal impact factors. Using a preferential attachment mechanism, we developed a model that integrated these networks and validated it with data from the American Physical Society (APS). While the correlations between reference counts, paper lifetime, and team sizes with scientific impact metrics are well-known, our findings demonstrate how these relationships vary depending on specific model parameters. For instance, increasing reference counts or reducing paper lifetime significantly boosts both journal impact factors and h-indexes, while expanding team sizes without adding new authors can artificially inflate h-indexes. These results highlight potential vulnerabilities in commonly used metrics and emphasize the value of modeling and simulation for improving bibliometric evaluations. [ABSTRACT FROM AUTHOR]

Here, we reflect on the long career in neuroendocrinology of a single, highly productive scientist ('Bob' Millar), by analysing his oeuvre of published papers through the lens of citation metrics. We use citation network analysis in a novel manner to identify the specific topics to which his papers have made a particular contribution, allowing us to compare the citations of his papers with those of contemporary papers on the same topic, rather than on the same broad field as generally used to normalise citations. It appears that citation rates are highest for topics on which Bob has published a relatively large number of papers that have become core to a tightly‐knit community of authors that cite each other. This analysis shows that an author's impact depends on the existence of a receptive community that is alert to the potential utility of papers from that author, and which uses, amplifies, extends and qualifies the contents of their papers—activities that entail reciprocal citation between authors. The obvious conclusion is that a scientist's impact depends on the use that his or her contemporaries make of his or her contributions, rather than on the contributions in themselves. [ABSTRACT FROM AUTHOR]

Many ranking algorithms and metrics have been proposed to identify high-impact papers. Both the direct citation counts and the network-based PageRank-like algorithms are commonly used. Ideally, the more complete the data on the citation network, the more informative the ranking. However, obtaining more data on citation relations is often costly and challenging. In some cases, obtaining the citation counts can be relatively simple. In this paper, we look into using the additional citation counts but without additional citation relations to form more informative metrics for identifying high-impact papers. As an example, we propose enhancing the original PageRank algorithm by combining the local citation network with the additional citation counts from a more complete data source. We apply this enhanced method to American Physical Society (APS) papers to verify its effectiveness. The results indicate that the proposed ranking algorithm is robust against missing data and can improve the identification of high-quality papers. This shows that it is possible to enhance the effectiveness of a network-based metric calculated on a relatively small citation network by including only the additional data of the citation counts, without the additional citation relations. [ABSTRACT FROM AUTHOR]

With tremendous growth in the volume of published scholarly work, it becomes quite difficult for researchers to find appropriate documents relevant to their research topic. Many research paper recommendation approaches have been proposed and implemented which include collaborative filtering, content-based, metadata, link-based and multi-level citation network. In this research, a novel Research paper Recommendation system is proposed by integrating Multiple Features (RRMF). RRMF constructs a multi-level citation network and collaboration network of authors for feature integration. The structure and semantic based relationships are identified from the citation network whereas key authors are extracted from collaboration network for the study. For experimentation and analysis, AMiner v12 DBLP-Citation Network is used that covers 4,894,081 academic papers and 45,564,149 citation relationships. The information retrieval metrices including Mean Average Precision, Mean Reciprocal Rank and Normalized Discounted Cumulative Gain are used for evaluating the performance of proposed system. The research results of proposed approach RRMF are compared with baseline Multilevel Simultaneous Citation Network (MSCN) and Google Scholar. Consequently, comparison of RRMF showed 87% better recommendations than the traditional MSCN and Google Scholar. [ABSTRACT FROM AUTHOR]

Based on the extracted information of retracted papers from the Retraction Watch Database and the citation information of these papers from the Web of Science, we uncovered the complex relationships of retracted papers in the molecular biology domain via a citation network. The basic characteristics (i.e., time and spatial patterns, reasons, publishers) of the retracted articles were studied. Citation network analysis, including community detection and text analysis, was carried out. Our main findings are as follows: (1) The overall number of retractions in this field has been increasing over time, and these retractions have been mainly in China and the USA. (2) Most retracted papers were for both "scientific error" and "misconduct" reasons. Among the 13 reasons given, errors in the data and images accounted for the largest proportion. (3) Community structure is obvious in the citation network we constructed. In communities with five or more nodes, the average self-citation rate account for 76%. In the three largest communities 1, 2, and 3, the self-citation rate are respectively 99%, 100% and 77%. In community 6, the self-catition rate is 17%. Other papers from different teams were published in the Journal of Cellular Biochemistry (4 papers). Tumor Biology (3 papers) or Febs Letters (1 paper). The self-citation rate of community 5 is 60.00%. Most papers are from Alfredo Fusco's team, and other ten papers are almost published in PLoS ONE. (4) The coupling relationship between citing-cited retraction reasons was revealed. Retractions and their citations were more likely to be retracted for the same reason. Most of the citing-cited papers from paper mills were published by the same publisher and even the same journal. (5) PI3K (an enzyme), WNT (a protein) and lncRNAs have recently been the major topics of retractions. [ABSTRACT FROM AUTHOR]

Purpose: This study identifies the main area and current dynamics of Corporate Philanthropy (CP) and offers recommendations for future research directions. Design/methodology/approach: Using bibliometrics, we examined a sample of 558 studies from Scopus, an online abstract and citation repository, to examine the research activities relating to CP between 1955 and 2022. Articles and authors with high citation impact scores are identified based on the publication’s position and significance within the field. We then explore contemporary issues, identify possible limitations towards the development of the literature, and propose new lines of inquiry for future studies. Findings: In addition to providing an in-depth analysis, this study discusses recent discoveries and shifts in mindsets pertaining to Corporate Philanthropy, taking into account previously documented findings which regarded CP disadvantageous to many companies mainly due to money outflow. The ‘agency theory’ considers the relationship between a principal and its hired representative. Past reports cited managers leveraging on CP as a way to justify unnecessary expenses. Meanwhile, the latest studies discovered that CP brings in various advantages to the companies such as additional profit avenues, enhanced visibility, and stronger market presence, benefiting its stakeholders and securing the sustainability of those companies. Future research on CP can be expanded through our suggested research directions. Research limitations/implications: This study focuses on using the Scopus database as the primary source of the journals. Hence, our findings may differ from the studies conducted using other databases. Originality/value: This is among the pioneer studies using Harzing and VOSviewer software to analyse the development of CP studies. Several future research agendas are generated due to the review, which focuses on annual publication trends, the most prolific authors, the most cited papers, the most prolific countries, the most prolific institutions and the most abundant sources. [ABSTRACT FROM AUTHOR]

This paper aims to understand how a group of academics cite each others' work through time, considering the simultaneous co-evolution of three networks representing their scientific collaboration, the journals in which they publish and institutional membership. It argues that both social and cognitive processes contribute to these dynamics. Two types of network mechanisms are considered specifically: closures by affiliation and closures by association. To assess whether these mechanisms generate the macro features of the network under study, we propose new features for three-mode multilevel networks such as the mixed geodesic distances, mixed degree distributions, and the mixed quadrilateral census. We investigate whether a micro-level model that considers the above-mentioned network mechanisms is able to correctly reproduce these features. We apply stochastic actor-oriented models (SAOMs) for one-mode and two-mode networks to link the micro-macro processes using a dataset of a scientific community of astronomers from 2013 to 2015. The results suggest that social relationships grounded on scientific collaboration and proximity based on institutional affiliation are more accurately suited to understanding the co-evolution of the network of citations than an alternative approach that merely considers cognitive-based networks measured as the similarity in publishing in the same journals. • We investigate how a group of academics cite each others' work through time considering the co-evolution of a multilevel network. • The co-evolution of a three-mode multilevel network is analysed considering cross-level effects. • New and already available measures for diagnostics are used for statistical models for social networks to identify how micro-mechanisms trigger different structures at the macro level. • Social relationships grounded on scientific collaboration and space proximity based on institutional affiliation are more accurately suited to understand the co-evolution of the networks in a scientific network. [ABSTRACT FROM AUTHOR]

Literature retrieval helps scientists find previous work that is relative to their own research or even get new research ideas. However, the discrepancy between retrieval results and the ultimate intention of citation is neglected by most literature retrieval models. Citation intent refers to the researcher's motivation for citing a paper. A citation intent graph with homogeneous nodes and heterogeneous hyperedges can represent different types of citation intents. By leveraging the citation intent information included in a hypergraph, a retrieval model can guide researchers on where to cite its retrieval result by understanding the citation behaviour in the graph. We present a ranking model called CitenGL (Citation Intent Graph Learning) that aims to extract citation intent information and textual matching signals. The proposed model consists of a heterogeneous hypergraph encoder and a lightweight deep fusion unit for efficiency trade-offs. Compared to traditional literature retrieval, our model fills the gap between retrieval results and citation intention and yields an understandable graph-structured output. We evaluated our model on publicly available full-text paper datasets. Experimental results show that CitenGL outperforms most existing neural ranking models that only consider textual information, which illustrates the effectiveness of integrating citation intent information with textual information. Further ablation analyses show how citation intent information complements text-matching signals and citation networks. [ABSTRACT FROM AUTHOR]

Drying microalgae is an important step in optimising their use in numerous industrial sectors since it reduces moisture content while conserving vital components. This method is very important for increasing the economic viability and sustainability of microalgae, which are known for their remarkable flexibility and bioactive chemical synthesis. Efficient drying processes protect the integrity of bioactive chemicals like antioxidants, anti-inflammatory agents, and anticancer compounds, as well as essential macronutrients like proteins, lipids, and carbs. Furthermore, drying preserves moisture-sensitive chemicals, improves shelf life, and concentrates lipids, making microalgae more appropriate for use in biofuels, pharmaceuticals, functional foods, cosmetics, and biotechnology. Despite microalgae's immense potential, market size and sustainability concerns remain, emphasising the need for constantly improving drying technology. This extensive study to a better understanding of the changing landscape of microalgae research, emphasising the critical significance of drying procedures in maximising the utility of microalgae in various industrial applications.

Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and Mean Reciprocal Rank (MRR) by 5.7%.

The present study examined the thematic composition and temporal evolution of social psychology through a co-citation network analysis of 80,350 articles published from 1970 through 2022. Six primary thematic clusters were identified: a broad “Classic Social Psychology” cluster most prominent in the 1970s and 1980s; “Traits & Affect” and “Social Cognition” clusters most influential in the 1990s; and “The Self,” “Intergroup Relations,” and “Big Five” clusters emerging after 2000. A small seventh cluster dedicated to COVID-19 and conspiracy theories emerged around 2021. These trends fit a narrative of generational shifts within distinct social and personality psychology traditions. [ABSTRACT FROM AUTHOR]

In recent years, the construction industry worldwide has shown significant interest in Building Information Modeling (BIM). This study aims to analyze the dissemination of knowledge about BIM in construction engineering applications using Main Path Analysis (MPA). The research sample comprises 3,761 papers related to BIM's application in the construction industry, sourced from the ISI Web of Science database. Initially, we investigate trends in paper publications, conduct country and journal analyses, and examine author statistics. Subsequently, we calculate traversal counts along the search path links to reveal the development trajectory of BIM. The trajectory of BIM's evolution in the construction industry can be divided into four stages as identified through the global key-route main path analysis: 1) BIM standardization; 2) Integration of completed building projects using BIM; 3) BIM applications in precast construction projects; and 4) BIM applications in land management. These findings provide a clear understanding of how BIM has been applied and evolved within the construction industry. [ABSTRACT FROM AUTHOR]

As a phased achievement of technological innovation, patent analysis holds extraordinary research significance. By constructing patent citation networks, scholars have proposed various centrality algorithms (such as citation count, PageRank, LeaderRank, etc.) for evaluating the quality and influence of patents. However, these centrality algorithms suffer from age bias, which means these algorithms are more inclined to obtain higher rankings for older patents, thus losing fairness to younger patents. Additionally, the selection of algorithm performance evaluation indicators is crucial. If the indicators are not chosen appropriately, the results may be affected. Therefore, based on the background of Chinese green patents, this paper develops an unbiased evaluation ranking algorithm to identify significant Chinese green patents earlier. The results demonstrate that the combination of the rescaled method and the AttriRank algorithm can effectively obtain the importance of patents, and provide a systematic and reasonable evaluation method for measuring patent value. [ABSTRACT FROM AUTHOR]

Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold-start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and mean reciprocal rank (MRR) by 5.7%. [ABSTRACT FROM AUTHOR]

The R package 'tosr' enables the construction of the Tree of Science (ToS), a metaphorical representation of scientific papers on a specific topic. The ToS's roots symbolize seminal works, the trunk stands for structural works, and the leaves depict the current literature. Traditionally, researchers have had to limit their ToS to data from a single database, such as Scopus or Web of Science (WoS). The 'tosr' package overcomes this limitation by allowing researchers to merge seed files from both Scopus and WoS, thereby facilitating a more comprehensive bibliometric analysis. This paper describes the development and application of the 'tosr' package, demonstrating its unique capabilities in creating a completer and more cohesive ToS and citation network for any scientific topic. By bridging the gap between these two major databases, 'tosr' offers researchers an unprecedented tool for scientometric research. [ABSTRACT FROM AUTHOR]

In this study, we analyze the changes over time in the complexity and structure of words used in article titles and the connections between articles in citation networks, focusing on the topic of artificial intelligence (AI) up to 2020. By measuring unpredictability in word usage and changes in the connections between articles, we gain insights into shifts in research focus and diversity of themes. Our investigation reveals correspondence between fluctuations in word complexity and changes in the structure of citation networks, highlighting links between thematic evolution and network dynamics. This approach not only enhances our understanding of scientific progress but also may help in anticipating emerging fields and fostering innovation, providing a quantitative lens for studying scientific domains beyond AI.

Finding appropriate and relevant papers about a project in various digital libraries with millions of scientific papers is challenging for researchers, resulting in a research innovation gap because of incomplete literature retrieval. A query-oriented paper recommendation (QPR) is a feasible way to improve the efficiency of literature retrieval in scientific research, and the graph-based method is one of the best solutions for QPR. However, current graph-based QPR methods still have the defeats of low precision and over-weighting. This paper proposes a query-oriented paper recommendation method using the Time Decay Heterogeneous Graph (TDHG) to improve the recommendation quality. TDHG is a four-layer heterogeneous graph combing the time decay characteristics in academic literature. We also used author rank to highlight contributions, the Author-Topic model to extend relations in heterogeneous graphs, and the Random Walk with Restart algorithm to rank papers. We designed three time-decay vectors and compared their impact on overcoming over-weighting caused by applying Random Walk with Restart algorithm to the original heterogeneous graph. Our experiments show linear time-decay vectors cannot balance the importance and timeliness of academic papers, while log time-decay vectors and sqrt time-decay vectors effectively solve the over-weighting problem. The experimental results show that the time-decay vector brings about an 11% and 8% improvement in Mean Average Precision (MAP) on the AAN and DBLP datasets, respectively. [ABSTRACT FROM AUTHOR]

In reiw of the importance of finding high-quality literature from massive literature, this paper proposed a literature evaluation index-citation structural diversity. It analyzed the feasibility of this model as a literature evaluation index through data analysis experiments on large-scale datasets, and then proposed an improved model to make it more suitable for citation network analysis. The data analysis results show that the structural diversity of citation network is significantly linearly related to the paper citation number, and the structural diversity of citation network is an essential factor affecting the citation number change trend after the publication of the literature. In 85.8% of the citation prediction experiments, structural diversity has im- proved the performance of models. The combined structural diversity has a prominent effect in the long-term citation prediction experiment, with a maximum increase of 22. 19%, an average increase of 14. 55% for the determination coefficient R2, and a maximum increase of 22.76%, an average increase of 17.34% for the mean square error MSE. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study on the development of qualitative social network analysis (QSNA) and its evolution over time, using the analysis of bibliographic networks. The dataset consists of articles from the Web of Science Clarivate Analytics database obtained by searching for the keyword"Social network* + (Qualitative OR Mixed method*)"(in total 21,823 publications). From the data, we constructed a citation network, which was followed by an evaluation of QSNA field's growth and the identification of the most cited works. Using the normalized Search path count weights, we extracted the Main path and Key-routes in the citation network. Starting from 1988, when the first works appeared, the field has grown significantly. The analysis of the citation network shows that there are two main scientific spheres where QSNA was developed—in the social science and the medical science. In the social science the main empirical research subjects are migrant networks and teacher's networks. In the medical science the research object moved from sexual contacts to social contacts in infection studies. [ABSTRACT FROM AUTHOR]

Copyright of Science of Gymnastics Journal is the property of Science of Gymnastics Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, the construction industry worldwide has shown significant interest in Building Information Modeling (BIM). This study aims to analyze the dissemination of knowledge about BIM in construction engineering applications using Main Path Analysis (MPA). The research sample comprises 3,761 papers related to BIM’s application in the construction industry, sourced from the ISI Web of Science database. Initially, we investigate trends in paper publications, conduct country and journal analyses, and examine author statistics. Subsequently, we calculate traversal counts along the search path links to reveal the development trajectory of BIM. The trajectory of BIM’s evolution in the construction industry can be divided into four stages as identified through the global key-route main path analysis: 1) BIM standardization; 2) Integration of completed building projects using BIM; 3) BIM applications in precast construction projects; and 4) BIM applications in land management. These findings provide a clear understanding of how BIM has been applied and evolved within the construction industry.

This study investigates the coevolution of coauthorship and citation networks and their influence on scientific metrics such as the h-index and journal impact factors. Using a preferential attachment mechanism, we developed a model that integrated these networks and validated it with data from the American Physical Society (APS). While the correlations between reference counts, paper lifetime, and team sizes with scientific impact metrics are well-known, our findings demonstrate how these relationships vary depending on specific model parameters. For instance, increasing reference counts or reducing paper lifetime significantly boosts both journal impact factors and h-indexes, while expanding team sizes without adding new authors can artificially inflate h-indexes. These results highlight potential vulnerabilities in commonly used metrics and emphasize the value of modeling and simulation for improving bibliometric evaluations.

Many ranking algorithms and metrics have been proposed to identify high-impact papers. Both the direct citation counts and the network-based PageRank-like algorithms are commonly used. Ideally, the more complete the data on the citation network, the more informative the ranking. However, obtaining more data on citation relations is often costly and challenging. In some cases, obtaining the citation counts can be relatively simple. In this paper, we look into using the additional citation counts but without additional citation relations to form more informative metrics for identifying high-impact papers. As an example, we propose enhancing the original PageRank algorithm by combining the local citation network with the additional citation counts from a more complete data source. We apply this enhanced method to American Physical Society (APS) papers to verify its effectiveness. The results indicate that the proposed ranking algorithm is robust against missing data and can improve the identification of high-quality papers. This shows that it is possible to enhance the effectiveness of a network-based metric calculated on a relatively small citation network by including only the additional data of the citation counts, without the additional citation relations.

The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations.

The purpose of this article is to examine the current state of scientific production, collaboration, and the effects of research publications on artistic gymnastics, as well as the most active researchers pursuing this area of study. Documents acquired from the Scopus database served as the basis for our investigation. MS Excel as well as VOS viewer were used to carry out the bibliometric analysis and network design. As a result, 430 original articles and scientific reviews in 22 Scopus subject areas were found in 167 journals. Most articles were published in Science of Gymnastics Journal (76). These articles are also most cited (294). There are 28 authors with five to fifteen articles whose citation form most of the citation network. Most of the artistic gymnastics research is published in journals that cover subjects such as medicine, health professions, and social science, with the first receiving the most citations.

Social Network Analysis is a discipline that represents social relationships as a network of nodes and edges. The construction of social network with clusters will contribute in sharing the common characteristics or behaviour of a group. Partitioning the graph into modules is said to be a community. Communities are meant to symbolize actual social groups that share common characteristics. Citation network is one of the social networks with directed graphs where one paper will cite another paper and so on. Citation networks will assist the researcher in choosing research directions and evaluating research impacts. By constructing the citation networks with communities will direct the user to identify the similarity of documents which are interrelated to one or more domains. This paper introduces the agglomerative technique algorithms and metrics to a directed graph which determines the most influential nodes and group of similar nodes. The two stages required to construct the communities are how to generate network with communities and how to quantify the network performance. The strength and a quality of a network is quantified in terms of metrics like modularity, normalized mutual information (NMI), betweenness centrality, and F-Measure. The suitable community detection techniques and metrics for a citation graph were introduced in this paper. In the field of community detection, it is common practice to categorize algorithms according to the mathematical techniques they employ, and then compare them on benchmark graphs featuring a particular type of assortative community structure. The algorithms are applied for a sample citation sub data is extracted from DBLP, ACM, MAG and some additional sources which is taken from and consists of 101 nodes (nc) with 621 edges € and formed 64 communities. The key attributes in dataset are id, title, abstract, references SLM uses local optimisation and scalability to improve community detection in complicated networks. Unlike traditional methods, the proposed LS-SLM algorithm is identified that the modularity is increased by 12.65%, NMI increased by 2.31%, betweenness centrality by 3.18% and F-Score by 4.05%. The SLM algorithm outperforms existing methods in finding significant and well-defined communities, making it a promising community detection breakthrough. [ABSTRACT FROM AUTHOR]

Background: This study aimed to analyze the relevance of different publications about microbiota on ocular diseases and their authors through a citation network analysis. In addition, the different research areas and the most cited publications have been identified. Methods: The bibliographic search was carried out through the Web of Science (WOS) database, using the following search term: “microbiota AND (vision OR eye OR visual)” for the period between 1995 and December 2022. The Citation Network Explorer and the CiteSpace software have been used to analyze the different publications. Results: 705 publications were found in the field of microbiota on ocular diseases, together with 1014 citation networks. The year 2022 was the year with more publications. The first authors with the highest number of publications in the microbiota on the ocular surface field were Chisari G, Chisari CG, and Li Y. This field is multidisciplinary, highlighting “microbiology” and “ophthalmology” as the main research areas. Publications were clustered into three main groups allowing the identification of the main research topics in this field. The principal was the composition and diversity of the bacterial community on the ocular surface of patients with several pathologies. Conclusion: It could be useful for researchers to choose suitable collaborators or projects to promote their research on the role of microbiota on ocular diseases, as well as to know the main research topics that are of major interest today.

This study investigates the strategic responses to the Covid-19 pandemic by academic research from the distinctive lens of corporate social responsibility (CSR). Specifically, it examines the evolution and trend of published academic research covering the CSR–Covid-19 nexus by highlighting key characteristics of published theoretical and empirical research linking the two elements. A total of 99 papers on CSR–Covid-19 nexus derived from Scopus database were analysed using bibliometrics, social network techniques and content analysis to examine the research activities since the start of the pandemic up to November 2021. The results indicate that the pandemic had effectively stimulated rapid growth of research interests examining the intertwined nature of CSR and Covid-19 as reflected in the swift proliferation of studies theoretically explaining and empirically testing the roles and impacts of CSR on various Covid-19 outcomes within myriads of domains, including but not limited to health, business practices, economics, and socials. The practical implications of this article lies on its ability to accentuate key research characteristics that early and/or mature researchers should consider when planning their future research trajectory on the link between CSR and Covid-19.

Purpose: The current research compares the representative elements (title, abstract, and keywords) of the articles that existed in the proposal references' citation network with the proposals’ elements. The other goal of this research is to calculate representative elements’ weighted average (title, abstract, and keywords) from a textual similarity perspective. Methodology: This is an applied and quantitative research that uses citation analysis and content analysis. The research sample is 3019 articles extracted from the citation network of 31 graduated students’ proposals (M.Sc. and Ph.D.) in Chemistry at Shiraz University. All English articles' titles in the proposals' references were searched on the Web of Science database, and each article's file and all articles’ files in its citation network were saved in Excel format. All retrieved files were merged into one file and sorted based on citation count to have the unit citation network for each user's proposal. Because some of the proposals had an extended citation network with more than a thousand articles, 100 articles with the greatest citation count of each network were analyzed to create uniformity and balance among the proposals’ citation networks. Next, the scale of textual similarity of 100 articles' representative elements with the greatest citation count in the citation network, was calculated with the proposal’s title, the proposal’s text, and the titles of the proposal’s references. The scale of textual similarity was checked using designed software based on the Python programming language and measuring the cosine similarity. Findings: The results of the Kruskal-Wallis test showed that there was a significant difference between the articles’ representative elements and the title, text, and references’ titles of the proposals from a textual similarity viewpoint; and in all three cases articles’ abstracts had the most textual similarity with the proposal elements, then, the title and keywords of the articles' citation network were in the second and third ranks; In addition, the representative elements’ weighted average was calculated. The obtained value was 0.62 for the abstract, 0.5 for the title, and 0.22 for the keywords, respectively. Conclusion: Despite the use of different platforms to measure the similarity between the documents searched and the documents desired by the user, there is still a distance to reach the ideal level. Until now, no research had used the representative elements of the articles that existed in the proposal references' citation network to measure the textual similarity with the proposal elements and had not evaluated their capability. The confirmation of textual similarity among the representative elements of the articles that existed in the proposal references' citation network with proposals’ elements, indicates that the student's proposal can be used as a platform for recommending related articles. Hence, the designers of scientific recommender systems, scientific information retrieval systems, digital libraries, and scientific social networks such as LinkedIn, Academia, and ResearchGate can use the elements of articles' citation networks to recommend related articles. In addition, considering the articles’ representative elements as independent units is important not only for similarity measurement but also for keyword expansion and suggesting the appropriate journal to the authors for publishing their articles. According to the determined weight of representative elements and to increase the efficiency of information systems, it is suggested that designers of such systems use the abstracts and the titles of the articles to measure the similarity and avoid calculating the similarity of the texts as a whole unit. This saves time, resources, and energy, presents better results, and users can reach their target and desired information more easily and faster than before. In addition, for indexing articles in databases and search engines, the articles' abstracts and titles can be prioritized to save financial resources and energy. [ABSTRACT FROM AUTHOR]

The interdisciplinary knowledge integration has become a typical feature in the development of modern science, which has promoted the production of original scientific achievements and major breakthroughs. The interdisciplinary network describes the knowledge transmission between disciplines, and the colored motif is the repeated subgraph in the network, reflecting the basic pattern of interdisciplinary. By assigning different colors to the nodes in the network, this paper introduces the analysis method of colored motifs into interdisciplinary research to explain the formation and evolution mechanism of interdisciplinary network at the meso-scale. We collected a set of datasets from the core database of Web of Science that contains 27,546 articles related to Perovskite Materials published from 2001 to 2021. After constructing interdisciplinary citation network, we further color network nodes according to their different discipline properties, and analyze evolution characteristics of uncolored and colored network motifs. By statistically analyzing the number of published papers and the proportion of interdisciplinary papers, our results show that the interdisciplinary evolution of Perovskite Materials is divided into two stages: the budding period from 2001 to 2014 and the developing period from 2015 to 2021, where the former stage is featured by knowledge integration, and the latter is dominated by knowledge diffusion. After analyzing colored network motifs, we find that increasing disciplinary categories and citations to different disciplinary knowledge will increase disciplinary diversity, but have a significant negative impact on disciplinary cohesion at the same time, where the former can exert greater influence. In addition, with the expansion of interdisciplinary breadth and reduction of disciplinary diffusion, hub nodes are conductive to emerge, thereby enhancing the cohesion of disciplines. [ABSTRACT FROM AUTHOR]

Textual reuse is a fundamental characteristic of traditional Buddhist literature preserved in various languages. Given the sheer volume of preserved Buddhist literature and the often-unmarked instances of textual reuse, the thorough analysis and evaluation of this material without computational assistance are virtually impossible. This study investigates the application of computer-aided methods for detecting approximately similar passages within Xuanzang's translation corpus and a selection of Abhidharma treatises preserved in Chinese translation. It presents visualizations of the generated network graphs and conducts a detailed examination of patterns of textual reuse among selected works within the Abhidharma tradition. This study demonstrates that the general picture of textual reuse within Xuanzang's translation corpus and the selected Abhidharma texts, based on computational analysis, aligns well with established scholarship. Thus, it provides a robust foundation for conducting more detailed studies on individual text sets. The methods employed in this study to create and analyze citation network graphs can also be applied to other texts preserved in Chinese and, with some modifications, to texts in other languages. [ABSTRACT FROM AUTHOR]

The digital revolution has heavily influenced digital game-based learning, yet as the revolution progresses, the conception of such learning has shifted along with the increasing complexity of the digital environment. Our study thus aims to identify research standing at this important juncture and to explain the shift in digital game-based learning research fields by adopting an integrated approach of main path analysis that yields this topic’s knowledge diffusion. Using key-route 8 to construct the path, we collect a total of 2156 articles and their data from The Web of Science database. From over 30 years of digital game-based learning development, 26 of the most influential studies are identified and visualized using Pajek software. The findings show two development phases for this field: exploring the role of gaming for educational purpose as well as facilitating learning performance. The research focus in the first phase prominently explores the potentials of digital games for educational purposes, and then the focus evolves in the second phase into actualizing the identified potentials. We propose a framework of digital game-based learning affordance actualization to explain these shifting phenomena in the specific research fields. Furthermore, unveiling the changing conception of digital game-based learning research is important for instructional designers, scholars, and educators to truly understand how technology can enhance teaching and facilitate learning performance.