Tokyo Institute of Technology · Business, Management and Accounting
Professor Yuya Kajikawa's research lab specializes in sustainability science and citation network analysis, focusing on the academic landscape, research trends, and knowledge diffusion in science and technology. The lab investigates the structural and topological properties of scientific citation networks using machine learning and network analysis to predict citation behavior and identify key research clusters. It also explores materials science, particularly the texture and preferred orientation control in sputter-deposited nitride films and CVD-grown silicon carbide, linking material growth mechanisms to experimental and numerical modeling. The lab bridges social science and materials science by integrating data-driven approaches to understand innovation dynamics and sustainability research.
Figures are computed from collected data and may differ slightly.
This paper reviews recent achievements in sustainability science and discusses the research core and framework of sustainability science. We analyze and organize papers published in three selected core journals of sustainability science: Sustainability Science, Proceedings of the National Academy of Sciences of the United States of America, and Sustainability: Science, Practice, & Policy. Papers are organized into three categories: sustainability and its definition, domain-oriented research, and
Sustainability is an important concept for society, economics, and the environment, with thousands of research papers published on the subject annually. As sustainability science becomes a distinctive research field, it is important to define sustainability clearly and grasp the entire structure, current status, and future directions of sustainability science. This paper provides an academic landscape of sustainability science by analyzing the citation network of papers published in academic jou
Texture control of sputter-deposited nitride films has provoked a great deal of interest due to its technological importance. Despite extensive research, however, the reported results are scattered and discussions about the origin of preferred orientation (PO) are sometimes conflicting, and therefore controversial. The aim of this study is to acquire a clear perspective in order to discuss the origin of PO of sputter-deposited nitrides. Among nitrides, we focus on titanium nitride (TiN), aluminu
Abstract In this article, we build models to predict the existence of citations among papers by formulating link prediction for 5 large‐scale datasets of citation networks. The supervised machine‐learning model is applied with 11 features. As a result, our learner performs very well, with the F1 values of between 0.74 and 0.82. Three features in particular, link‐based Jaccard coefficient difference in betweenness centrality , and cosine similarity of term frequency–inverse document frequency vec
Abstract In this article, we investigated the factors determining the capability of academic articles to be cited in the future using a topological analysis of citation networks. The basic idea is that articles that will have many citations were in a “similar” position topologically in the past. To validate this hypothesis, we investigated the correlation between future times cited and three measures of centrality: clustering centrality, closeness centrality, and betweenness centrality. We also
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