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Dimensions was introduced as an alternative bibliometric database to the well-established Web of Science (WoS) and Scopus, however all three databases have fundamental differences in coverage and content, resultant from their owners' indexation philosophies. In light of these differences, we explore here, using a citation network analysis and assessment of normalized citation impact of overlapping publications indexed in all three databases, whether the three databases offer structurally different perspectives of the bibliometric landscape or if they are essentially homogenous substitutes. Our citation network analysis of core and exclusive 2016–2018 publications revealed a large set of core publications indexed in all three databases that are highly self-referential. In comparison, each database selected a set of exclusive publications that appeared to hold similarly low levels of relevance to the core set and to one another, with slightly more internal communication between exclusive publications in Scopus and Dimensions than WoS. Our comparison of normalized citations for 41,848 publications indexed in all three databases found that German sectors were valuated as more impactful in Scopus and Dimensions compared to WoS, particularly for sectors with an applied research focus. We conclude that the databases do present structurally different perspectives, although Scopus and Dimensions with their additional circle of applied research vary more from the more base research-focused WoS than they do from one another. [ABSTRACT FROM AUTHOR]

Graph representation learning is a significant challenge in graph signal processing (GSP). The flourishing development of graph neural networks (GNNs) provides effective representations for GSP. To effectively learn from graph signals, we propose a regularized graph neural network based on approximate fractional order gradients (FGNN). The regularized graph neural network propagates the information between neighboring nodes. The approximation strategy for calculating fractional order derivatives avoids falling into fractional order extrema and overcomes the high computational complexity of fractional order derivatives. We further prove that such an approximation is feasible and FGNN is unbiased towards the global optimization solution. Extensive experiments on citation and community networks show that the proposed FGNN has improved recognition accuracy and convergence speed than vanilla FGNN. The five datasets of different sizes and domains confirm the great scalability of our proposed method. [ABSTRACT FROM AUTHOR]

Horizon scanning for innovative technologies that might be applied to medical products and requires new assessment approaches to prepare regulators, allowing earlier access to the product for patients and an improved benefit/risk ratio. The purpose of this study is to confirm that citation network analysis and text mining for bibliographic information analysis can be used for horizon scanning of the rapidly developing field of AI-based medical technologies and extract the latest research trend information from the field. We classified 119,553 publications obtained from SCI constructed with the keywords "conventional," "machine-learning," or "deep-learning" and grouped them into 36 clusters, which demonstrated the academic landscape of AI applications. We also confirmed that one or two close clusters included the key articles on AI-based medical image analysis, suggesting that clusters specific to the technology were appropriately formed. Significant research progress could be detected as a quick increase in constituent papers and the number of citations of hub papers in the cluster. Then we tracked recent research trends by re-analyzing "young" clusters based on the average publication year of the constituent papers of each cluster. The latest topics in AI-based medical technologies include electrocardiograms and electroencephalograms (ECG/EEG), human activity recognition, natural language processing of clinical records, and drug discovery. We could detect rapid increase in research activity of AI-based ECG/EEG a few years prior to the issuance of the draft guidance by US-FDA. Our study showed that a citation network analysis and text mining of scientific papers can be a useful objective tool for horizon scanning of rapidly developing AI-based medical technologies. [ABSTRACT FROM AUTHOR]

Certain innovative technologies applied to medical product development require novel evaluation approaches and/or regulations. Horizon scanning for such technologies will help regulators prepare, allowing earlier access to the product for patients and an improved benefit/risk ratio. This study investigates whether citation network analysis and text mining of scientific papers could be a tool for horizon scanning in the field of immunology, which has developed over a long period, and attempts to grasp the latest research trends. As the result of the analysis, the academic landscape of the immunology field was identified by classifying 90,450 papers (obtained from PubMED) containing the keyword "immune* and t lymph*" into 38 clusters. The clustering was indicative of the research landscape of the immunology field. To confirm this, immune checkpoint inhibitors were used as a retrospective test topic of therapeutics with new mechanisms of action. Retrospective clustering around immune checkpoint inhibitors was found, supporting this approach. The analysis of the research trends over the last 3 to 5 years in this field revealed several candidate topics, including ARID1A gene mutation, CD300e, and tissue resident memory T cells, which shows notable progress and should be monitored for future possible product development. Our results have demonstrated the possibility that citation network analysis and text mining of scientific papers can be a useful objective tool for horizon scanning of life science fields such as immunology. [ABSTRACT FROM AUTHOR]

Abstract Background Numerous efforts have been poured into annotating the wealth of knowledge contained in biomedical articles. Thanks to such efforts, it is now possible to quantitatively explore relations between these annotations and the citation network at large scale. Results With the aid of several large and small annotation databases, this study shows that articles share annotations with their citation neighborhood to the point that the neighborhood’s most common annotations are likely to be those appearing in the article. Conclusions These findings posit that an article’s citation neighborhood defines to a large extent the article’s annotated content. Thus, citations should be considered as a foundation for future knowledge management and annotation of biomedical articles.

With the increase in scientific publications and the diversification of research areas, science has become complex and interdisciplinary. Discovering important knowledge has become difficult even for researchers in specific domains. Previously proposed keyphrase extraction methods focus mainly on detecting intensively discussed topics in specific domains but do not distinguish concepts with the interdisciplinary spread from those discussed in narrow areas. Here, we propose a diffusion meme score that evaluates the knowledge diffusion distance in a paper citation network. The distance between papers that contain specific terms is measured by the network embedding space of the citation network. Using 57 million publication records from 48 years of Scopus, we evaluated newly appearing terms in and after 1975 in biomedical science papers using the proposed indicator. Approximately half of the top 20 terms were related to Nobel Prize or Clarivate Citation Laureates, and the top terms of the indicators were more likely to appear in Wikipedia than terms extracted using existing methods. Moreover, the top terms were unlikely to include specific minor diseases, which are often extracted using existing methods. Therefore, the diffusion meme score evaluates important terms from scientific literature and citation networks that are more interdisciplinary. Our method improves the understanding of young researchers regarding domains, the development of the history of science, and the evaluation of researcher contributions.

Delays in the propagation of scientific discoveries across scientific communities have been an oft-maligned feature of scientific research for introducing a bias towards knowledge that is produced within a scientist's closest community. The vastness of the scientific literature has been commonly blamed for this phenomenon, despite recent improvements in information retrieval and text mining. Its actual negative impact on scientific progress, however, has never been quantified. This analysis attempts to do so by exploring its effects on biomedical discovery, particularly in the discovery of relations between diseases, genes and chemical compounds. Results indicate that the probability that two scientific facts will enable the discovery of a new fact depends on how far apart these two facts were originally within the scientific landscape. In particular, the probability decreases exponentially with the citation distance. Thus, the direction of scientific progress is distorted based on the location in which each scientific fact is published, representing a path-dependent bias in which originally closely-located discoveries drive the sequence of future discoveries. To counter this bias, scientists should open the scope of their scientific work with modern information retrieval and extraction approaches. [ABSTRACT FROM AUTHOR]

Abstract In this work we give a community detection algorithm in which the communities both respects the intrinsic order of a directed acyclic graph and also finds similar nodes. We take inspiration from classic similarity measures of bibliometrics, used to assess how similar two publications are, based on their relative citation patterns. We study the algorithm’s performance and antichain properties in artificial models and in real networks, such as citation graphs and food webs. We show how well this partitioning algorithm distinguishes and groups together nodes of the same origin (in a citation network, the origin is a topic or a research field). We make the comparison between our partitioning algorithm and standard hierarchical layering tools as well as community detection methods. We show that our algorithm produces different communities from standard layering algorithms.

Understanding a country's scientific situation is essential for any country. Common indicators for measuring a country's scientific capability are the number of publications and citations. In this study, we design a new method, the citation induced scientific capability identification (CISCI) method, to measure a country's scientific capability from the perspective of the scientific roles the country undertakes in scientific research. Then, we employ this method to analyze countries' scientific capability based on downloaded citation data in the field of economics and business. Finally, we check the applicability of our designed method and explore countries' scientific capability in the accounting and physics disciplines. The results show that the scientific capability of 158 countries has been changing for many years. Every country has different scientific capabilities in different scientific roles, and China's scientific capability has improved dramatically. The CISCI method is applicable for analyzing scientific capability in scientific research.

Delays in the propagation of scientific discoveries across scientific communities have been an oft-maligned feature of scientific research for introducing a bias towards knowledge that is produced within a scientist’s closest community. The vastness of the scientific literature has been commonly blamed for this phenomenon, despite recent improvements in information retrieval and text mining. Its actual negative impact on scientific progress, however, has never been quantified. This analysis attempts to do so by exploring its effects on biomedical discovery, particularly in the discovery of relations between diseases, genes and chemical compounds. Results indicate that the probability that two scientific facts will enable the discovery of a new fact depends on how far apart these two facts were originally within the scientific landscape. In particular, the probability decreases exponentially with the citation distance. Thus, the direction of scientific progress is distorted based on the location in which each scientific fact is published, representing a path-dependent bias in which originally closely-located discoveries drive the sequence of future discoveries. To counter this bias, scientists should open the scope of their scientific work with modern information retrieval and extraction approaches.

Objective: We aim to answer four questions. First, with the increasing number of publications, is there a concentration in specific subjects, or on the contrary, a dispersion, amplifying the span of themes related to entrepreneurship? Second, is there a hierarchy of subjects, in the sense that some of them constitute the “core” of entrepreneurship? Third, are they connected with other established research areas? Finally, it is possible to identify papers that are influential, acting as hubs in the cluster’s formation? Method: We developed an original version of the computational procedure proposed by Shibata et al (2008), which allows us to understand the diversity of the different sub-areas of the topic investigated, reducing the need for specialist supervision. Originality / Relevance: We developed and applied a method to capture the formation and evolution of research areas in entrepreneurship literature, via direct citation networks, allowing us to understand the iteration between the different research sub-areas. Results: The dispersion is a feature of entrepreneurship as field research, with a hierarchy between research areas, indicating an emergent organization in the expansion processes. We concluded that research on entrepreneurship consists of specialization, that is, by application in niches. JEL Code: L26, O30, O38, O25

As Foucault’s ironic approach to history is to suggest that looking at our past can help us to change the future, I contend that connections make the best disruptions. To wit, I introduce citation networks to make connections between articles and citations as a way of disrupting assumptions about what has been and can be done in the name of mathematics education research. The purpose of this paper is to introduce citation networks, a novel method for identifying field connections, and a theory of spatiality that deploys the metaphors of bubbles and foams to imagine disruptions. After introducing the method and theory that guide this investigation, I introduce and describe the foams of the research published in the Journal for Research in Mathematics Education and Educational Studies in Mathematics during the 2010s. In doing so, I show what topics of inquiry constitute the dominant research foci of our field, or at least of our field as it is published in these mainstream mathematics education journals. Later, I read these maps with a critical lens towards the foci to discern what is marginalized and excluded, namely the urban. This research builds on earlier work that names the ghettoization of urban mathematics education research and provides a critical interrogation of the ways that urban research is marked and marginalized within dominant mathematics education research journals. I conclude by situating the role of the Journal of Urban Mathematics Education as a place to blow bubbles, a place to reconfigure what we can see, say, think, and do in the name of (urban) mathematics education research.

The number of scientific papers is increasing in the rapid growth. How to make paper acquisition efficient and provide effective citation recommendation is essential for researchers. Although the application of scientific citation recommendation has shown great improvements, the in-depth mining and fusion of various types of information has been ignored. In this paper, we propose a scientific citation recommendation model integrating network and text representation (SCR-NTR), which comprises data acquisition, feature representation, feature fusion and link prediction. We compare the network representation and text representation, respectively, and select the models performing best in the pre-experiment as the sub-models of SCR-NTR. The method of vector concatenate fusion is employed to fuse two kinds of information, and the logistic regression classifier is selected to carry out the link prediction. The extensive experiments reveal that our model can effectively improve the performance on citation recommendation. In addition, the effect of different fusion methods and different classifiers are investigated, and qualitative analysis is conducted to further verify the effectiveness of SCR-NTR. The experimental results show that leveraging both network and text representation can enhance the recommendation performance, and the heterogenous network representation learning can capture richer semantic information of the given network than the homogeneous one. [ABSTRACT FROM AUTHOR]

Journal impact indicators are useful measures to determine the relative importance of journals within a given discipline. By recasting the journal cross-citation matrix as a directed network, journals can be represented by nodes and journal-journal citations correspond to weighted directed links between nodes. In this form, it is possible to use graph-theoretic approaches to determine a ranking of nodes based on the structure of the links they are embedded in. Based on the Power-Weakness paradigm originally introduced by Ramanajucharyulu for ranking players in a tournament, we show how this idea can be modified under a bibliometric setting to find journals that contribute proportionate inbound and outbound citation flows within a homogeneous citation network to the extent that the power to influence and the weakness to be influenced by other journals are in near equal measure. We study two journal systems based on the Library and Information Sciences field based on a citation window in 2012 and another in 2018. [ABSTRACT FROM AUTHOR]

Background: The mission of medicines regulatory agencies is to ensure the timely access of innovative products for patients to improve public health. Thus, regulators should foresee evolving technologies and build expertise prior to reviewing innovative products. Novel modalities and new classes of therapeutics in biological or cell-based products represent a regulatory challenge because of knowledge gaps, as exemplified by the unexpected cytokine release syndrome in the first-in-human clinical trial of the CD28 super-agonist. Meanwhile, recent treatments harnessing T cell co-signaling pathways provide an opportunity for investigation. Therefore, this study aimed to systematically identify and evaluate novel modalities for T cell immunity to assess the need for regulatory guidance.Methods: A PubMed search was carried out using the query, “immun* AND t lymph*” to select publications. Subsequently, a citation network was created, followed by clustering and text mining to identify the modalities and classes of therapeutics under development.Results and Discussion: Analysis of the top 20 clusters revealed research domains characterized by keywords such as immune checkpoint antibody, chimeric antigen receptor (CAR)-T cells, microbiota, exosome, regulatory T cells, unconventional T cells, and vaccines. After reviewing the pharmacological concepts, clinical trial information, and available guidance, we presented a perspective on the future development of guidance for these domains.Conclusion: Bibliometric analyses identified a set of innovative modalities targeted for drug development with which regulatory guidance is going to catch up. This strategy could help in the successful development of upcoming modalities to ensure readiness for clinical application as part of horizon scanning.

In this paper, I conducted a systematic review of the economic development literature from 1959 to 2020 to reveal the ebbs and flows of major research topics, combining the text mining technique with the bibliographic analysis. Topics such as “regional development” and “sustainable development” have gained importance over the decades, while “development strategy” has lost its prominent status. New topics such as “climate change” and “developing countries” have emerged in recent years. An analysis of the citation network reveals three distinctive research trajectories: One engages with the concepts of the creative class, human capital, clusters, and art and culture. Another centers around the topic of “regional development”, and a third, smaller group studies “new industrial district”. This study helps researchers understand the evolution of the roadmap in the field of economic development, properly situate their own work in the literature, identify influential citations, and discover novel research topics in need of further exploration.

Citation counts is a crucial factor in evaluating the quality of research papers. Therefore, it is vital to accurately predict citation counts and explore the mechanisms underlying citations. In this study, we focus on predicting the citation counts in the field of statistics. We collect 55,024 academic papers published in 43 statistics journals between 2001 and 2018. Furthermore, we collect and clean a high-quality dataset and then construct multi-layer networks from different perspectives, including journal networks, author citation networks, co-citation networks, co-authorship networks, and keyword co-occurrence networks. Additionally, we extract 77 factors for citation counts prediction, including 22 traditional and 55 network-related factors. To address the issues of zero-inflated and over-dispersed citation counts, a neural network model is designed to achieve high prediction accuracy. Furthermore, we adopt a leave-one-feature-out approach to investigate the importance of these factors. The proposed neural network model achieves an MAE value of 7.352, which outperforms other machine learning models in the comparison. Thus, this study provides a useful guide for researchers to predict citation counts and can be easily extended to other research fields. • Extracting factors based on multi-layer academic networks. • Extracting research topics using keyword co-occurrence network. • Using the neural network model to predict the citation counts. • Indicating network-related factors possess strong predictive. performance. • Measuring factor importance using the leave-one-feature-out method. [ABSTRACT FROM AUTHOR]

Via the idea of social computing, a simple experimental model is developed as a reference for exploring the variation of journal impact factors, particularly between different disciplines. Our model intends to simulate the publication and citation behaviors of the articles in journals belonging to a similar discipline, in a distributed manner. Based on the citation networks generated, the mechanism of influence from several fundamental factors to the trend of impact factor is better revealed. These factors include the average review cycle, the average number of references, and yearly distribution of references. The analysis also reveals a satisfactory capability of the model for approximation between actual data and simulation results.

Article influence ranking is an effective way to reduce information redundancy and improve the efficiency of article retrieval. A large number of ranking models for network items have been employed for the ranking of article influence, such as PageRank and Spamming-resistant Expertise Analysis and Ranking. However, the effectiveness of article influence ranking based on the models of PageRank and SPEAR declines with the rapid growth of academic datasets, because of the increasing complexity of citation network. In order to take a rich set of contextual structures of citation context into consideration, we propose a visualization system VAIR for the citation context-based article influence ranking. Firstly, the word2vec model, a renowned technique in the field of natural language processing, is applied to transform articles into vectorized representations according to citation context. Then, a novel citation context-based article influence ranking model is designed according to the complex relationships quantified in a semantic vectorised space. Several visual designs are implemented, allowing users to perceive and compare the ranking results visually and intuitively. A set of user-friendly interactions are provided in the visualization framework, enabling users to explore the desirable article influence and obtain deep insights into the ranking model. Moreover, a series of case studies and comparison experiments are carried out based on real-world datasets, which further demonstrate the effectiveness of our algorithm for article influence ranking.

One of the disciplines behind the science of science is the study of scientific networks. This work focuses on scientific networks as a social network having different nodes and connections. Nodes can be represented by authors, articles or journals while connections by citation, co-citation or co-authorship. One of the challenges in creating scientific networks is the lack of publicly available comprehensive data set. It limits the variety of analyses on the same set of nodes of different scientific networks. To supplement such analyses we have worked on publicly available citation metadata from Crossref and OpenCitatons. Using this data a workflow is developed to create scientific networks. Analysis of these networks gives insights into academic research and scholarship. Different techniques of social network analysis have been applied in the literature to study these networks. It includes centrality analysis, community detection, and clustering coefficient. We have used metadata of Scientometrics journal, as a case study, to present our workflow. We did a sample run of the proposed workflow to identify prominent authors using centrality analysis. This work is not a bibliometric study of any field rather it presents replicable Python scripts to perform network analysis. With an increase in the popularity of open access and open metadata, we hypothesise that this workflow shall provide an avenue for understanding scientific scholarship in multiple dimensions.

Aims: The study aims to carry out a bibliometric analysis of the research on magnet hospital from 1988‐2020. Design: A statistical and visualization bibliometric analysis was conducted between February‐May 2020. Methods: The information was extracted from Web of Science by the topic advanced search strategy. HistCite‐Pro, Excel, GunnMap, BibExcel and VOSviewer were used to extract, integrate and visualize the bibliometric information. Furthermore, the research contents of the core‐authors were explored and summarized by reviewing all the published articles. Results: Articles (N = 396) published in 107 journals were retrieved in the Web of Science from 1988‐2020. The USA (N = 319) and University of Pennsylvania (N = 38) were the most productive country and institution respectively. Author co‐citation and bibliographic‐coupling network analysis identified that Aiken, Kramer, Laschinger, Schmalenberg, Lake and Johantgen were the major contributors and leaders in this field. According to the research content of the main contributors and keyword co‐occurrence analysis result, the research focus could be divided into four parts: the mechanism leading to the outcomes differences for magnet hospital, the evidence‐based practice, the outcomes comparison between magnet hospital and non‐magnet hospital and the practice environment. Conclusion: This bibliometric study offered a comprehensive overview of current study status of magnet hospital and this area has been drawn increasing attention over the years. However, the research on magnet hospital is still relative limited and needs to be paid more attention, especially in developing countries. Impact: This study showed the most contributed countries, journals, institutions, authors and the main themes related to magnet hospital, which would help researchers find potential partners, optimize and deepen future research, such as enhancing the magnet hospital research in developing countries, improving the evidence‐based research and considering the impact caused by different culture. Furthermore, this study can also give suggestions for magnet hospital policy makers. [ABSTRACT FROM AUTHOR]

Author name disambiguation is an important research topic in the academic information retrieval community. Existing methods rely either on feature engineering on rich attributes information or on relationship information to obtain documents' similarity, but seldom consider the complementarity and the correlation between them. The feature engineering on attributes, especially on rich text information, could capture the global semantic concepts, while the relationship information could encode local structural proximity in multiple academic networks. To bridge the gap between semantic and relationship information in author name disambiguation, this paper presents a joint representation learning approach, which could encode both semantic and relationship information into a common low dimensional space. Specifically, the proposed method consists of four modules: (1) semantic embedding module; (2) relationship embedding module; (3) semantic and relationship joint embedding module; and (4) clustering module. Experimental results demonstrate that the proposed joint representation learning approach consistently outperforms the state-of-the-art methods on three benchmarks. [ABSTRACT FROM AUTHOR]

Graph representation learning is a significant challenge in graph signal processing (GSP). The flourishing development of graph neural networks (GNNs) provides effective representations for GSP. To effectively learn from graph signals, we propose a regularized graph neural network based on approximate fractional order gradients (FGNN). The regularized graph neural network propagates the information between neighboring nodes. The approximation strategy for calculating fractional order derivatives avoids falling into fractional order extrema and overcomes the high computational complexity of fractional order derivatives. We further prove that such an approximation is feasible and FGNN is unbiased towards the global optimization solution. Extensive experiments on citation and community networks show that the proposed FGNN has improved recognition accuracy and convergence speed than vanilla FGNN. The five datasets of different sizes and domains confirm the great scalability of our proposed method.

Background: Numerous efforts have been poured into annotating the wealth of knowledge contained in biomedical articles. Thanks to such efforts, it is now possible to quantitatively explore relations between these annotations and the citation network at large scale. Results: With the aid of several large and small annotation databases, this study shows that articles share annotations with their citation neighborhood to the point that the neighborhood's most common annotations are likely to be those appearing in the article. Conclusions: These findings posit that an article's citation neighborhood defines to a large extent the article's annotated content. Thus, citations should be considered as a foundation for future knowledge management and annotation of biomedical articles. [ABSTRACT FROM AUTHOR]

Researchers need to formulate their achievements as research papers. Representative references are essential to high-quality papers. Academic citation recommendation refers to providing the recommendation of citations for the author of papers when they write. With the help of citation recommendation, researchers can improve the efficiency of writing academic papers and reduce the omission of important related literature. To achieve this goal, some methods were proposed. Many of them used citation networks to learn the representation of papers and chose references, they tended to ignore the content properties of papers. There are also some methods used partial properties to recommend citation. But their performance can be further improved. In this paper, we propose a citation recommendation method based on context correlation. We use two neural network models to learn the representations of papers and their references, then calculate the context similarity of them. Besides, we also introduce the publishing time and authority of papers, two key properties of papers for citation evaluation. In the experiment section, we compare our method with other methods and evaluate the performance of different properties choice in our method, it shows that our method outperforms some baselines and the combination of the dimensions including time, authority and context performs better. [ABSTRACT FROM AUTHOR]

Over the last two decades, the focus of studies on the spatial organization of urban systems has shifted noticeably from classical theories such as "the central place theory" to utilizing novel theories based on the network essence of intercity relations, such as "the central flow theory". This field of urban research, which has emerged under the term "external urban relations", has developed through integrating contributions from many disciplines. However, different methodological traditions and interdisciplinary contexts in which the research is conducted are leading to ambiguity in how we understand and measure intercity relations. This study systematically reviews and analyzes the body of this new literature. It does so by innovatively employing a scientometric mapping approach, which is a combination of bibliometric and citation network analysis methods. This review is based on a publication set of 145 papers published on the subject since 1995. The number of publications covered by the Web of Science (WoS) shows a significant increase in the most recent years. The current study identifies 20 papers as playing a pivotal role in the evolution of the literature on urban external relations. Furthermore, the results revealed five conceptualizations before the 1990s at the classical theories era, along with two renowned conceptualizations during the 1990s (the transition decade), which have had the most impact on changes in the metamorphosis of theoretical fundamentals. The present developments are influenced by a wide range of various concepts, methods, and empirical approaches. Two different subfields (schools) of thought known as "world/global cities", and "polycentricity" are identified by systemically integrating traditional top-down and bottom-up review methods. Their different aspects were also discussed. [ABSTRACT FROM AUTHOR]

Traditionally, publication citation networks are regarded as acyclic, that is, no loops in the network as an earlier published article cannot cite a later published article. However, due to the accessibility of pre-print versions of articles, there might be some loops in a publication citation network. This article presents a descriptive statistic on loops in publication citation networks of computer science and physics by employing a network-based indicator, namely, strongly connected component (SCC). By employing computer science and physics disciplines publications from the Web of Science database as examples, this article examines the count of loops, how the count changes over time and how the count relates to the published year difference between publications within the loop in the citation network. Some common structural patterns are also extracted and analysed; we observe that the two disciplines share the most frequent patterns though there exist some minor differences. Moreover, we find that self-citations in terms of authors, authors' institutions and journals contribute to the formation of loops in publication citation networks. [ABSTRACT FROM AUTHOR]

Identification of important works and assessment of importance of publications in vast scientific corpora are challenging yet common tasks subjected by many research projects. While the influence of citations in finding seminal papers has been analysed thoroughly, citation-based approaches come with several problems. Their impracticality when confronted with new publications which did not yet receive any citations, area-dependent citation practices and different reasons for citing are only a few drawbacks of them. Methods relying on more than citations, for example semantic features such as words or topics contained in publications of citation networks, are regarded with less vigour while providing promising preliminary results. In this work we tackle the issue of classifying publications with their respective referenced and citing papers as either seminal, survey or uninfluential by utilising semantometrics. We use distance measures over words, semantics, topics and publication years of papers in their citation network to engineer features on which we predict the class of a publication. We present the SUSdblp dataset consisting of 1980 labelled entries to provide a means of evaluating this approach. A classification accuracy of up to.9247 was achieved when combining multiple types of features using semantometrics. This is +.1232 compared to the current state of the art (SOTA) which uses binary classification to identify papers from classes seminal and survey. The utilisation of one-vector representations for the ternary classification task resulted in an accuracy of.949 which is +.1475 compared to the binary SOTA. Classification based on information available at publication time derived with semantometrics resulted in an accuracy of.8152 while an accuracy of.9323 could be achieved when using one-vector representations. [ABSTRACT FROM AUTHOR]

Citation networks of scientific publications offer fundamental insights into the structure and development of scientific knowledge. We propose a new measure, called intermediacy, for tracing the historical development of scientific knowledge. Given two publications, an older and a more recent one, intermediacy identifies publications that seem to play a major role in the historical development from the older to the more recent publication. The identified publications are important in connecting the older and the more recent publication in the citation network. After providing a formal definition of intermediacy, we study its mathematical properties. We then present two empirical case studies, one tracing historical developments at the interface between the community detection literature and the scientometric literature and one examining the development of the literature on peer review. We show both conceptually and empirically how intermediacy differs from main path analysis, which is the most popular approach for tracing historical developments in citation networks. Main path analysis tends to favour longer paths over shorter ones, whereas intermediacy has the opposite tendency. Compared to the main path analysis, we conclude that intermediacy offers a more principled approach for tracing the historical development of scientific knowledge.

Background and aim: The aim of this study was to analyze and draw up a citation network of published schizophrenia-related resources in Iran from 2012 to 2016 so that this citation network provides a guidance for researchers and acquisition in information centers by specifying the status of resources and researchers in Iran. Material and methods: The current study was conducted through citation analysis method and using social network analysis. The study population was 5303 citations extracted from the list of 180 papers published in the field of schizophrenia in Iran. NodeXL software was used to analyze and draw the citation network and Excel software was applied for statistical calculations. Findings: There are 3 components in the citation network of schizophrenia-related articles. In the largest component, there are 2395 people who have cited each other 5287 times, and the network diameter is 12. The average distance in the network is 5.39 and the network density is less than 0.5, indicating low density of citations. Moreover, in terms of document type, articles (78.76%) have been cited more frequently than other resources. The most cited resource in the citation network is "Comprehensive Textbook of Psychiatry" compiled by Harold Kaplan and Benjamin James Sadock and the most cited author is Benjamin James Sadock. Conclusion: Generally, the citation network in schizophrenia is a dispersed network with a very low density in Iran, representing that the potential relationships have not been formed among authors.

With the rapid growth of scientific publications, research paper recommendation which suggests relevant research papers to users can bring great benefits to researchers. In this paper, we focus on the problem of recommending conference papers to the conference attendees. While most of the related existing methods depend on the content-based filtering, we propose a unified conference paper recommendation method named $CPRec$ , which exploits both the contents and the authorship information of the papers. In particular, besides the contents, we exploit the relationships between a user and the authors of a paper for recommendation. In our method, we extract several features for a user-paper pair from the citation network, the coauthor network, and the contents, respectively. In addition, we derive a user’s pairwise preference towards the conference papers from the user’s bookmarked papers in each conference. Furthermore, we employ a pairwise learning to rank model which exploits the pairwise user preference to learn a function that predicts a user’s preference towards a paper based on the extracted features. We conduct a recommendation performance evaluation using real-world data and the experimental results demonstrate the effectiveness of our proposed method.

Different research traditions have developed over time to study the quantitative aspects of information and knowledge production, such as scientometrics, bibliometrics, librametrics, informetrics, cybermetrics, webometrics, or altmetrics. These information metrics, or iMetrics, as they were labeled by Milojević and Leydesdorff in Scientometrics 95(1):141–157, 2013, are unified by the usage of quantitative data analysis, applying various statistical methods and techniques and are often used to supplement and complement each other. Representing different research traditions, they jointly form a common research field, a "discipline with many names". In this article, we look at the development of iMetrics field and its evolution over time using bibliometric network analysis and identify its common basis, formed by the most important publications, journals, scholars and topics. The dataset consists of articles from the Web of Science database (26,414 records with complete descriptions). Analyzing the citation network, we evaluate the field's growth and identify the most cited works. Using the Search path count (SPC) approach, we extract the Main path, Key routes paths, and Link islands in the citation network. The results show that in the last forty years the number of published papers increased, and it doubles every 8 years; the number of single author papers dropped from 50 to 10 %, and the number of papers authored by 3 or more authors is increasing. We make the conclusions about the field's development and its current state. We also present the main authors, journals and keywords from the field, which form its common basis. [ABSTRACT FROM AUTHOR]

Summary: Scholarly big data network is a complex network of citations from research community across the globe. An effective scholar assessment structure is essential for scholars, researchers, and universities. The research publications are an important factor in the university rankings. The fast growth of digital publishing and scholarly data is progressively challenging every day. These days, the scholarly data can be accessed effortlessly through various data analysis techniques. In this paper, a new framework is designed for big scholarly data, and an amoeboid approach article‐optimal citation flow (A‐OCF) is used to find the optimal flow of citations in the big scholarly data network. A novel modern metrics for article quality (MMAQ) metric is proposed to identify the quality of articles. The performance analysis uses different bibliometric measures, including the impact factor citations, conference proceedings citations, and other citations with the purpose of measuring the quality of cited articles. The scholar analytic results are equated with existing techniques. We have also analyzed central articles in a research area through the MMAQ metrics and tested it with benchmark data sets. [ABSTRACT FROM AUTHOR]

The article discusses the publication activity of scientists in the field of studying the consequences of US economic sanctions against Iran, and their impact on the development of science and the economy in this country. The paper considers the dynamics of publication activity in the field of biomedicine of Iranian scientists over the past 20 years. Increased sanctions have led to a shortage of medicines and a lack of access to modern treatment methods. The population does not have access to modern medicines. Patients cannot afford to buy expensive medicines. The general deterioration of the working and living conditions of the Iranian population led to a significant decrease in the activity of scientists. The publication activity of Iranian scientists has significantly decreased after the imposition of sanctions against Iran due to US disagreement with the country's nuclear policy. The present study has used modern methods and scientific bases of documents. The data have been gathered from freely available electronic resources: PubMed and Dimensions. Graphs and diagrams were built using the analytical tools of electronic resources described above by the VOSviewer visualization tool. This paper has examined a new trans-corpus approach to studying the publication activity of Iranian scientists based on a comparison of the dynamics of changes in the number of publications and citations of the country's scientists in the field of medicine, depending on the imposed international sanctions and the influence of science on the development of medicine within the framework of the sanctions policy. [ABSTRACT FROM AUTHOR]

The most fundamental way to measure the impact of a scientific publication is using the number of citations it received. Though citation count and its variants are widely adopted, they have been pointed out to be poor proxies for a paper's quality because a citation might result from different reasons. It is thus crucial to quantify the true relevance of the cited papers to the citing paper. There are already some efforts in the literature devoted to addressing this issue, yet a well-accepted method is still lacking, possibly due to the absence of standard ground truth data for comparing different methods. In this paper, we propose a simple method using a local diffusion process on citation networks for identifying the key references for each scientific publication. The effectiveness and of the method are validated in a subset of the American Physical Society data in which the key references are mentioned in the abstract of papers. We further define an effective citation metric for quantifying the actual impact of each paper and its evolution. The effective citation metric additionally reveals the citation preference of research at journal and country levels. [ABSTRACT FROM AUTHOR]

This article reveals how citation networks can be used to understand and demonstrate the ways design serves to integrate other bodies of knowledge. Analysis focused on two corpuses of peer-reviewed journal articles, representing two bodies of knowledge. The first corpus consisted of 277 articles with the words "Design Thinking" in title. The second corpus contained 296 articles with the words "Network Science" in the title. Paper citation and document co-citation networks of individual and combined corpuses were analyzed. The overlap of paper citation networks was used to synthesize similarities, differences, and opportunities for further integration. [ABSTRACT FROM AUTHOR]

Originality has self-evident importance for science, but objectively measuring originality poses a formidable challenge. We conceptualise originality as the degree to which a scientific discovery provides subsequent studies with unique knowledge that is not available from previous studies. Accordingly, we operationalise a new measure of originality for individual scientific papers building on the network betweenness centrality concept. Specifically, we measure the originality of a paper based on the directed citation network between its references and the subsequent papers citing it. We demonstrate the validity of this measure using survey information. In particular, we find that the proposed measure is positively correlated with the self-assessed theoretical originality but not with the methodological originality. We also find that originality can be reliably measured with only a small number of subsequent citing papers, which lowers computational cost and contributes to practical utility. The measure also predicts future citations, further confirming its validity. We further characterise the measure to guide its future use. [ABSTRACT FROM AUTHOR]