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Link prediction in social networks refers toward inferring the new interactions among the users in near future. Citation networks are constructed based on citing each other papers. Reciprocal link prediction in citations networks refers toward inferring about getting a citation from an author, whose work is already cited by you. The paper aims to discuss these issues.

In this paper, the authors study the extent to which the information of a two-way citation relationship (called reciprocal) is predictable. The authors propose seven different features based on papers, their authors and citations of each paper to predict reciprocal links.

Extensive experiments are performed on CiteSeer data set by using three classification algorithms (decision trees, Naive Bayes, and support vector machines) to analyze the impact of individual, category wise and combination of features. The results reveal that it is likely to precisely predict 96 percent of reciprocal links. The study delivers convincing evidence of presence of the underlying equilibrium amongst reciprocal links.

It is not a generic method for link prediction which can work for different networks with relevant features and parameters.

This paper predicts the reciprocal links to show who is citing your work to collaborate with them in future.

The proposed method will be helpful in finding collaborators and developing academic links.

The proposed method uses reciprocal link prediction for bibliographic networks in a novel way.

This study aims to provide guidelines for exploring the research landscape in developing countries by gauging the prospects of growth, research impact and innovation. This study interrogates, analyses and visualizes the impact, nuances and evolution of stated research themes. For this purpose, this study presents an in-depth analysis of publications and citations indexed in Pakistani journals as a case study.

A bibliometric analysis of 46,034 publications published in Pakistan-based journals uncovers the research landscape of Scopus-indexed scientific literature – using various statistical and network-based approaches. Using VOSviewer and SPSS tools, the publication data has been analysed in relation to the open access status of papers, the number of authors, discipline, research theme and international co-authorship.

This study’s analyses reveal that while Pakistani journals are attracting international contributions from several countries, including India, Malaysia and Indonesia, no journal falls into the Scopus-defined top Quartile, i.e. the Q1 category. The analyses also highlight that only half (47%) of the publications received citations, whereas the other half remained uncited. Furthermore, open access publications received significantly higher citations than subscribed/traditional publications (print/online subject to toll access).

To the best of the authors’ knowledge, this is the first impact study of its kind that critically analyses the research landscape of Pakistani journals, especially in the context of the efforts of the higher education commission of Pakistan to promote research culture in the country. This study also provides analytical insights and policy guidelines for improving the quality of research published in Pakistani journals. This study can be replicated for other developing nations to provide guidelines and sustainable pathways for scientific growth in pursuit of uplifting nations by allocating resources for developing science and technology.

The purpose of this paper is to present a novel approach for mining scientific trends using topics from Call for Papers (CFP). The work contributes a valuable input for researchers, academics, funding institutes and research administration departments by sharing the trends to set directions of research path.

The authors procure an innovative CFP data set to analyse scientific evolution and prestige of conferences that set scientific trends using scientific publications indexed in DBLP. Using the Field of Research code 804 from Australian Research Council, the authors identify 146 conferences (from 2006 to 2015) into different thematic areas by matching the terms extracted from publication titles with the Association for Computing Machinery Computing Classification System. Furthermore, the authors enrich the vocabulary of terms from the WordNet dictionary and Growbag data set. To measure the significance of terms, the authors adopt the following weighting schemas: probabilistic, gram, relative, accumulative and hierarchal.

The results indicate the rise of “big data analytics” from CFP topics in the last few years. Whereas the topics related to “privacy and security” show an exponential increase, the topics related to “semantic web” show a downfall in recent years. While analysing publication output in DBLP that matches CFP indexed in ERA Core A* to C rank conference, the authors identified that A* and A tier conferences not merely set publication trends, since B or C tier conferences target similar CFP.

Overall, the analyses presented in this research are prolific for the scientific community and research administrators to study research trends and better data management of digital libraries pertaining to the scientific literature.

This study reviews the methods found in the literature for the ranking of authors, identifies the pros and cons of these methods, discusses and compares these methods. The purpose of this paper is to study is to find the challenges and future directions of ranking of academic objects, especially authors, for future researchers.

This study reviews the methods found in the literature for the ranking of authors, classifies them into subcategories by studying and analyzing their way of achieving the objectives, discusses and compares them. The data sets used in the literature and the evaluation measures applicable in the domain are also presented.

The survey identifies the challenges involved in the field of ranking of authors and future directions.

To the best of the knowledge, this is the first survey that studies the author ranking problem in detail and classifies them according to their key functionalities, features and way of achieving the objective according to the requirement of the problem.

Citation analysis is an important measure for the assessment of quality and impact of academic entities (authors, papers and publication venues) used for ranking of research articles, authors and publication venues. It is a common observation that high-level publication venues, with few exceptions (Nature, Science and PLOS ONE), are usually topic specific. The purpose of this paper is to investigate the claim correlation analysis between topic specificity and citation count of different types of publication venues (journals, conferences and workshops).

The topic specificity was calculated using the information theoretic measure of entropy (which tells us about the disorder of the system). The authors computed the entropy of the titles of the papers published in each venue type to investigate their topic specificity.

It was observed that venues usually with higher citations (high-level publication venues) have low entropy and venues with lesser citations (not-high-level publication venues) have high entropy. Low entropy means less disorder and more specific to topic and vice versa. The input data considered here were DBLP-V7 data set for the last 10 years. Experimental analysis shows that topic specificity and citation count of publication venues are negatively correlated to each other.

This paper is the first attempt to discover correlation between topic sensitivity and citation counts of publication venues. It also used topic specificity as a feature to rank academic entities.

The latest developments in Data Science and in advanced Scientometrics set a very challenging context for the analysis and the understanding of human behavior toward the design of value adding library services and sophisticated information systems. The purpose of this paper is to present an innovative research that integrates the creation and the consumption of scientific knowledge across regions. From a human behavior point of view, this is significant since it provides an advanced decision-making layer for bringing together researchers from all over the world.

More specific in this paper, the authors analyze the production and consumption of scientific knowledge across the regions in an important field of sustainable and renewable energy – using publications and citations data indexed in Scopus. As a case study, the authors select the USA a major producer of scientific publications in the field. At first, the authors identify the topics produced by the USA. Further topics produced by the scientific communities outside the USA that consume the knowledge produced by the USA are identified. The authors generate topics by employing the proposed topic model with distance matrix – an extension of classic latent Dirichlet allocation model.

The results show that research topics produced by the USA are consumed in different international contexts, interestingly. Consuming the knowledge produced by the USA, Chinese scientific community heavily produces topics related to biomass – to produce renewable energy. In contrast, Japanese scientific community produces topics related to fuel cell – used for the production of hybrid and electronic vehicles. Whereas the Taiwanese scientific community shows remarkable competency in solar cells. Among the European nations, while the German scientific community produces topics related to photovoltaic, the French scientific community covers topics related to Energy Storage and Green Chemistry. The authors believe that such analyses may be helpful in establishing more effective multi-national research collaborations by understating the actual consumption of produced knowledge.

Overall, the study provides a new dimension to comprehensively understand production and consumption of knowledge using scientific literature. From a human behavior analysis view in the context of sophisticated library systems, this is a significant contribution.

The use of advanced Data Mining and computing methods for deriving critical insights for the use of scientific knowledge is a bold action toward the global knowledge society vision.

This paper presents a comprehensive study using bibliometric and social network analysis (SNA) to depict the academic community, research hotspots and the correlation between research performance and social network measurements within Library Hi Tech.

Publications from Library Hi Tech between 2010 and 2022 are reviewed and analysed through coauthorship analysis, co-occurrence analysis, SNA and the Spearman rank correlation test.

The annual number of publications in Library Hi Tech increased from 2016 to 2022, indicating that this research has gradually gained global attention. The USA and China are the most significant contributors to the relevant publications. Scholars in this field mainly engage in small-scale cooperation. Academic libraries, digital libraries, libraries, information technology and COVID-19 were hot topics during the study period. In light of the COVID-19 pandemic, there was a marked increase in research on healthcare. Academic interest in the internet of Things and social media has proliferated recently and may soon attract more attention. Spearman rank correlation analysis shows that research performance (i.e. publication count and citation count) is significantly and positively correlated with social network measurements (i.e. degree centrality, betweenness centrality, closeness centrality and eigenvector centrality) in studies of Library Hi Tech.

This paper reveals a systematic picture of the research landscape of Library Hi Tech and provides a potential guide for future research. The relationship between scientific research performance and social network measurements can be objectively identified based on statistical knowledge.

Recently, reverse logistics (RL) has become more prominent due to growing environmental concerns, social responsibility, competitive advantage and high efficiency by customers because of the expansion of product selection and shorter product life cycle. However, effective implementation of RL results in some direct advantages, the most important of which is winning customer satisfaction that is vital to a firm’s success. Therefore, paying attention to customer feedback in supply chain and logistics processes has recently increased so that manufacturers have decided to transform their RL into customer-centric RL. Hence, this paper aims to identify the features of a mobile phone which affect consumer purchasing behaviour and to analyse the interrelationship among them to develop a framework for customer-centric RL. These features are studied based on website analysis of several mobile phone manufacturers. The special focus of this paper is on social media data (Twitter) in an attempt to help the decision-making process in RL through a big data analysis approach.

A portfolio of mobile phone features that affect consumer’s mobile phone purchasing decisions has been taken from website analysis by several mobile phone manufacturers to achieve this objective. Then, interrelationships between the identified features have been established by using big data supplemented with interpretive structural modelling (ISM). Apart from that, cross-impact matrix multiplication, applied to classification analysis, was carried out to graphically represent these features based on their driving power and dependence.

During the study, it has been observed from the ISM that the chip (F5) is the most significant feature that affects customer’s buying behaviour; therefore, mobile phone manufacturers realize that this is to be addressed first.

The focus of this paper is on social media data (Twitter) so that experts can understand the interaction between mobile phone features that affect consumer’s decisions on mobile phone purchasing by using the results.

Many higher education institutions are investigating the possibility of developing predictive student success models that use different sources of data available to identify students that might be at risk of failing a course or program. The purpose of this paper is to review the methodological components related to the predictive models that have been developed or currently implemented in learning analytics applications in higher education.

Literature review was completed in three stages. First, the authors conducted searches and collected related full-text documents using various search terms and keywords. Second, they developed inclusion and exclusion criteria to identify the most relevant citations for the purpose of the current review. Third, they reviewed each document from the final compiled bibliography and focused on identifying information that was needed to answer the research questions

In this review, the authors identify methodological strengths and weaknesses of current predictive learning analytics applications and provide the most up-to-date recommendations on predictive model development, use and evaluation. The review results can inform important future areas of research that could strengthen the development of predictive learning analytics for the purpose of generating valuable feedback to students to help them succeed in higher education.

This review provides an overview of the methodological considerations for researchers and practitioners who are planning to develop or currently in the process of developing predictive student success models in the context of higher education.