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A common consensus in the literature is that the citation profile of published articles in general follows a universal pattern - an initial growth in the number of citations within the first two to three years after publication followed by…
Charting the intellectual evolution of a scientific discipline is crucial for identifying its core contributions, challenges, and future directions. The IISE Annual Conference proceedings offer a rich longitudinal archive of the Industrial…
Recently, graph neural networks have become a hot topic in machine learning community. This paper presents a Scopus based bibliometric overview of the GNNs research since 2004, when GNN papers were first published. The study aims to…
In recent times, the academic ecosystem has seen a tremendous growth in number of authors and publications. While most temporal studies in this area focus on evolution of co-author and citation network structure, this systemic inflation has…
Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Traditional approaches, including heuristic and learning-based…
This systematic review of the research literature on retrieval-augmented generation (RAG) provides a focused analysis of the most highly cited studies published between 2020 and May 2025. A total of 128 articles met our inclusion criteria.…
Growth of science is a prevalent issue in science of science studies. In recent years, two new bibliographic databases have been introduced which can be used to study growth processes in science from centuries back: Dimensions from Digital…
The increase in the number of publications has made more difficult for authors to situate their work within previous literature, especially on subjects studied from different disciplinary viewpoints. Besides, new data analysis techniques…
In this study, I employ a multifaceted comprehensive scientometric approach to explore the intellectual underpinnings of AI and ML in financial research by examining the publication patterns of articles, journals, authors, institutions, and…
Scientific document embeddings contain a variety of rich features which can be harnessed for downstream tasks such as recommendation, ranking, and clustering. We explore which tangible insights can be drawn from scientific document…
We represent collaboration of authors in computer science papers in terms of both affiliation and collaboration networks and observe how these networks evolved over time since 1960. We investigate the temporal evolution of bibliometric…
Background: Introduced in 2010, the sub-discipline of gerontologic biostatistics (GBS) was conceptualized to address the specific challenges in analyzing data from research studies involving older adults. However, the evolving technological…
Since its first release in 2004, the CAS Journal Ranking, a ranking system of journals based on a citation impact indicator, has been widely used both in selecting journals when submitting manuscripts and conducting research evaluation in…
The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task,…
A bibliometric methodology for scanning for emerging science and technology areas is described, where topics in the science, technology and innovation enterprise are discovered using Latent Dirichlet Allocation, their growth rates are…
Bibliometric studies are a rare undertaking in the field of English language teaching, especially at a journal level. To celebrate the 30th anniversary of TEFLIN Journal, this study exhibits a bibliometric portrait of its publication,…
Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scenarios. The majority of existing research efforts primarily…
Neuromorphic computing and spiking neural networks aim to leverage biological inspiration to achieve greater energy efficiency and computational power beyond traditional von Neumann architectured machines. In particular, spiking neural…
Clustering algorithms or methods for GPS trajectories are in constant evolution due to the interest aroused in part of the scientific community. With the development of clustering algorithms considered traditional, improvements to these…
This paper explores the current trending research areas in the field of Computer Science (CS) and investigates the factors contributing to their emergence. Leveraging a comprehensive dataset comprising papers, citations, and funding…