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Papers published in top conferences or journals is an important measure of the innovation ability of institutions, and ranking paper acceptance rate can be helpful for evaluating affiliation potential in academic research. Most studies only focus on the paper quality itself, and apply simple statistical data to estimate the contribution of institutions. In this work, a novel method is proposed by combining different types of features of affiliation and author to predict the paper acceptance at the institutional level. Based on the history of the paper published, this work firstly calculates the affiliation scores, constructs an institutional collaboration network and analyzes the importance of the institutions using network centrality measures. Four measures about the authors’ influence and capability are then extracted to take the contributions of authors into consideration. Finally, a random forest algorithm is adopted to solve the prediction problem of paper acceptance. As a result, this paper improves the ranking of the paper acceptance rate NDCG@20 to 0.865, which is superior to other state-of-the-art approaches. The experimental results show the effectiveness of proposed method, and the information between different types of features can be complementary for predicting paper acceptance rate.

The volume of research articles in digital repositories is increasing. This spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries. The problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories. In the traditional recommendation approaches, the results of the query miss many high-quality papers, in the related work section, which are either published recently or have low citation count. To overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles. Many research paper recommendation approaches have been implemented which includes collaborative filtering-based, content-based, and citation analysis-based techniques. The collaborative filtering-based approaches primarily use paper-citation matrix for recommendations, whereas the content-based approaches only consider the content of the paper. The citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest. It is therefore very difficult for a recommender system to recommend high-quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information. The proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors' collaboration network finds key authors from those papers. The papers selected by this hybrid approach are then recommended to the user. The results have shown that our proposed method performs exceedingly well as compared with the state-of-the-art existing systems, such as Google scholar and multilevel simultaneous citation network.

A concise quantitative method is established for identifying ‘seed’ papers in sciences. The method is set up following h-type metrics based on co-citation network analysis. With defining original-seed (O-Seed) and dominant-seed (D-Seed) by measurable h-strength and second-order h-type degree centrality, O-seed resembles to be a ‘root’ and D-seed develops to become ‘stem’. Using dataset from Web of Science (WoS), the ‘seed’ papers in research fields of graphene, genome editing, and h-set studies are identified. Graphene D-Seed paper and genome editing D-Seed paper are representative outputs of the 2010 Nobel Prize in Physics and the 2020 Nobel Prize in Chemistry respectively. H-set O-Seed and D-Seed are the same paper that first proposed the concept of h-index. The ‘seed’ papers are characterized by not only high citations, but also network structure and core function in sciences.

This study explored the relationship between syntactic complexity and rhetorical functions and the relationship between syntactic complexity and level of move/step realization in learner-generated research paper introductions. We created a corpus of 79 introductions written by undergraduate L2 students at intermediate to high-intermediate levels. We annotated the introductions for moves and steps, measured their syntactic complexity at the global, clausal, and phrasal levels, and rated their level of move/step realization across four levels (no, low, intermediate, and high). We conducted one-way MANOVA, one-way ANOVA, Pearson correlation, and stepwise multiple linear regression analyses. We found sentences realizing Reviewing items of previous research function to be significantly longer than other steps in Move1 and to contain significantly more clausal and phrasal complexity features. Sentences performing Claiming centrality function also demonstrated significantly more phrasal complexity features as measured by complex nominals per clause. We also found a higher level of phrasal complexity in introductions with a higher level of move/step realization, which can be attributed to introductions including Step1 and Step3, rather than containing all steps. Our findings imply that there is value in developing explicit instructions on how to use complex structures in performing rhetorical steps in research paper introductions. © 2021 Elsevier Ltd

Purpose This paper aims to assess the predictive power of authorship properties determined at the time of publishing conference papers on future citations of conference papers in computer and information science. Design/methodology/approach This study examined 21 factors – related to all authors and to the first author – as potentially predictive of citation counts. Specifically, the study assessed properties of author's academic performance, degree of collaboration and topological properties of their research collaboration networks. Findings The results of comparing all authors with first authors indicate that the all author-related factors have a significantly higher power for explaining conference paper citation counts than the first author-related factors. Moreover, among the all author-related factors, the degree centrality before the target paper made the largest contribution. Originality/value This is one of the first attempts to focus on the relationship of author characteristics to conference papers. This is also one of only a few studies to expand prior research, which limited its bibliometric foci to journal articles, to conference papers.

Purpose This paper aims to discuss the evolution of the cultural tourism market and what its future may be. Design/methodology/approach Instead of seeing the cultural tourism market as homogeneous, a segmentation model was developed based on the centrality of culture in the overall decision to travel and the depth of experience sought. Findings The model verified five segments, including: the purposeful cultural tourist, the sightseeing cultural tourist, the serendipitous cultural tourist, the casual cultural tourist and the incidental cultural tourist. Originality/value This framework has transformed how we think about cultural tourists, as it provides a better understanding of the differences in behavior and preferences for certain types of experiences. It is also a tool to develop more effective marketing communication strategies and serves as the basis to evaluate the market significance of this activity.

This article examines the extent to which existing network centrality measures can be used (1) as filters to identify a set of papers to start reading within a journal and (2) as article-level metrics to identify the relative importance of a paper within a journal. We represent a dataset of published papers in the Public Library of Science (PLOS) via a co-citation network and compute three established centrality metrics for each paper in the network: closeness, betweenness, and eigenvector. Our results show that the network of papers in a journal is scale-free and that eigenvector centrality (1) is an effective filter and article-level metric and (2) correlates well with citation counts within a given journal. However, closeness centrality is a poor filter because articles fit within a small range of citations. We also show that betweenness centrality is a poor filter for journals with a narrow focus and a good filter for multidisciplinary journals where communities of papers can be identified.

The breakthrough potentials of research papers can be explained by their boundary-spanning qualities. Here, for the first time, we apply the structural variation analysis (SVA) model and its affiliated metrics to investigate the extent to which such qualities characterize a group of Nobel Prize winning papers. We find that these papers share remarkable boundary-spanning traits, marked by exceptional abilities to connect disparate and topically-diverse clusters of research papers. Further, their publications exert structural variations on the scale that significantly alters the betweenness centrality distributions of existing intellectual space. Overall, SVA not only provides a set of leading indicators for describing future Nobel Prize winning papers, but also broadens our understanding of the similar prize-winning properties that may have been overlooked among other regular publications., 27 pages, 8 figures, 9 tables. Submitted to Frontiers in Research Metrics and Analytics

With the increasing frequency of scientific cooperation, research on the impact of scientific cooperation on scholars’ scientific performance has become a hot topic in academic research. This paper mainly uses quantitative research methods such as bibliometrics and social network analysis to analyze the correlation between degree centrality, L-index, and scientific performance of the Chinese author of Physical Education and Training Science and draws the following conclusions: (1) degree centrality is positively correlated with the number of papers, average citations per paper, and H-index and (2) L-index is positively correlated with the number of papers, average citations per paper, and H-index. Unlike previous studies on scientific collaboration networks and scholars’ performance that focused on traditional network characteristics indicators, this study introduces a new network characteristics indicator, L-index, through which the degree of collaboration between authors and other important authors in the research field can be better assessed, which also provides a new direction for research related to research collaboration networks and scholars’ performance; at the same time, it also provides a theoretical basis for subsequent research on the utility of scientific collaboration, a reference for the assessment of the research performance of scholars in other disciplines, and a theoretical reference for more scientific and comprehensive prediction of scholars’ research performance in the future.

Through an ethnography of a terrorism trial that followed bomb‐blasts in Delhi in 2008, this article seeks to understand the centrality of files and documentary practices to the production of legal truth. By following key documents regarding the case against one man I call Fahad, I argue that the truth produced in a trial crucially depends a chain of seemingly insignificant certificatory practices‐the signatures, countersignatures, stamps, and seals that appear on documents. What emerges in the account I provide is that juridical truth is less a matter of finding ‘what really happened,’ and more about the competition between narratives that depend on the certificatory correctness of humble sheets of paper.

The Panama Papers comprise one of the most recent influential leaks containing detailed information on intermediary companies (such as law firms), offshore entities and company officers, and serve as a valuable source of insight into the operations of (approximately) 214,000 shell companies incorporated in tax havens around the globe over the past half century. Entities and relations in the papers can be used to construct a network that permits, in principle, a systematic and scientific study at scale using techniques developed in the computational social science and network science communities. In this paper, we propose such a study by attempting to quantify and profile the importance of entities. In particular, our research explores whether intermediaries are significantly more influential than offshore entities, and whether different centrality measures lead to varying, or even incompatible, conclusions. Some findings yield conclusions that resemble Simpson’s paradox. We also explore the role that jurisdictions play in determining entity importance.

Previous research recognises the strategic role of vanguard projects in providing their initiators with avenues for entering new markets or gaining mastery over innovative technologies. This study makes a contribution to this research by focusing on the extent to which vanguard projects are under control and serve the interests of their principal initiators and the other actors involved. Simultaneously, the present study contributes to project management research by applying historical case study methodology on Eurocan, a vanguard project that a major Finnish forest industry firm Enso established in the mid-1960s to the wilderness of British Columbia, Canada. Our historical analysis encourages regarding vanguard projects as relay races in which several actors participate, largely in unanticipated ways. This is especially because the initiation of vanguard projects appears to be characterised by both the heterogeneity of the actors involved, a wide variety of actions taken by these actors to increase their centrality in the project organisation as well as abrupt changes among them and their relative importance over the project lifecycle. Together these characteristics make vanguard projects particularly prone to influence from external actors and events. publishedVersion

The COVID-19 pandemic, and ensuing physical distancing measures, poses challenges for researchers in the field of interprofessional care. Pandemic management has highlighted the centrality of interprofessional working to effective healthcare delivery during crises. It is essential to find ways to maintain interprofessional research that has commenced, while also designing research to capture important learning from pandemic management and response. However, it also creates opportunities for new research projects and novel research designs. This discussion paper explores ways of adapting existing research methodologies and outlines potential avenues for new research. Specifically, considerations to bear in mind when designing interprofessional research during the pandemic include research ethics and integrity, research design, data collection methods, research opportunities, implications and limitations. Interprofessional research can continue to make a valuable contribution in informing global responses to COVID-19 and in planning for future global health crises. We call for, insofar as possible, for interprofessional research to continue to be developed during this time. Postprint

The service sector has become the most important part of the global economy. Thus, Service Business: An International Journal (SB) was launched in 2007 to deliver innovative studies that can provide resonating insights to researchers and practitioners in services. This study analyzed 311 articles published in SB during the past 10+ years (2007–2017) by conducting network text analysis (NTA). The published papers’ keywords link relations were analyzed to create network maps of research topics, ranging from traditional to emerging ideas of researchers. Influential research topics and their clusters were identified using centrality and community analyses. Research trends were identified for the first 5 years (Volume 1–5) and later years (Volume 6–11) separately through NTA. The results reveal interesting change in research topics over time. Common keywords of studies published in the first 5 years were: Service, Innovation, Customer, Strategy, Tourism, and Quality. The primary frequent keywords of articles published during 2012–2017 were: Customer, Quality, Satisfaction, Innovation, Mobile, and Technology. The results of the study shed valuable insights to the researchers and practitioners in the service sector. Applications of keyword network building and analysis methods provided in this study would be helpful to researchers who are interested in exploring trends of emerging new topics.

It is generally accepted that the latest research findings are published as research papers in journals. Social network analysis (SNA) can offer a perspective from which to analyze technological developments. This work analyzes the development of lithium-ion batteries (LIBs) and electrolytes by SNA based on papers from 2011 to 2015. Three types of social networks, country-, institution-, and research paper-based networks were used. Country-based network analysis shows that major ten countries have a close relationship with respect to technology development. Institution-based network analysis revealed that 48 top institutions publish the most prolifically and that a “small-world” effect exists within the network, indicating excellent knowledge transfer among institutions through citations. Several institutions occupy the center position within the network and have a prominent influence in knowledge transfer as judged by centrality analysis. Research paper-based social networks, consisting of high-citation articles, are grouped into six technology clusters, which exhibit a broad range of knowledge transfer processes regardless of the battery and electrolyte types, indicating underlying closely interwoven relationships. The knowledge transfer path can describe the technology evolution in detail. An overview of technological development status can be thus obtained by this proposed three-level analysis method.

Citations between scientific papers and related bibliometric indices, such as the $h$-index for authors and the impact factor for journals, are being increasingly used - often in controversial ways - as quantitative tools for research evaluation. Yet, a fundamental research question remains still open: to which extent do quantitative metrics capture the significance of scientific works? We analyze the network of citations among the $449,935$ papers published by the American Physical Society (APS) journals between 1893 and 2009, and focus on the comparison of metrics built on the citation count with network-based metrics. We contrast five article-level metrics with respect to the rankings that they assign to a set of fundamental papers, called Milestone Letters, carefully selected by the APS editors for "making long-lived contributions to physics, either by announcing significant discoveries, or by initiating new areas of research". A new metric, which combines PageRank centrality with the explicit requirement that paper score is not biased by paper age, is the best-performing metric overall in identifying the Milestone Letters. The lack of time bias in the new metric makes it also possible to use it to compare papers of different age on the same scale. We find that network-based metrics identify the Milestone Letters better than metrics based on the citation count, which suggests that the structure of the citation network contains information that can be used to improve the ranking of scientific publications. The methods and results presented here are relevant for all evolving systems where network centrality metrics are applied, for example the World Wide Web and online social networks. An interactive Web platform where it is possible to view the ranking of the APS papers by rescaled PageRank is available at the address \url{http://www.sciencenow.info}., Main text (pp. 1-12) + Appendices (pp. 13-18)