Kazuki Nakajima (中嶋 一貴)
Assistant Professor
Graduate School of Systems Design
Tokyo Metropolitan University, Japan
E-mail: nakajima (at) tmu.ac.jp
Go to researchmap (in Japanese)
News
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(2024/03) I will be delivering an oral presentation on the latest work at NetSci2024.
HyperNEO: Inference of community structure in attributed hypergraphs.
K. Nakajima and T. Uno. (2024). [arXiv] -
(2024/01) Our new paper is now on arXiv.
Inferring community structure in attributed hypergraphs using stochastic block models.
K. Nakajima and T. Uno. (2024). [arXiv] [code] -
(2023/11) Our paper has been published in the Journal of Informetrics.
Quantifying gender imbalance in East Asian academia: Research career and citation practice.
K. Nakajima, R. Liu, K. Shudo, N. Masuda. (2023). [paper]
[プレスリリース（神戸大学）]
[朝日新聞デジタル]
[ITMedia NEWS]
Research
A huge amount of social data on individuals' connections and behavior contributes to the understanding of social structure and dynamics. However, it is not uncommon that such data is unoptimized for research use (e.g., it is limited in accessibility or subject to social biases). I am working to develop computational methods for analyzing such social data.My areas of interest are
- Computational Social Science
- Network Science
- Science of Science
- Social Graph Restoration via Random Walk Sampling.
K. Nakajima, K. Shudo. Proc. ICDE. (2022). - Estimating the Bot Population on Twitter via Random Walk Based Sampling.
M. Fukuda, K. Nakajima, K. Shudo. IEEE Access. (2022). - Estimating Properties of Social Networks via Random Walk considering Private Nodes.
K. Nakajima, K. Shudo. Proc. KDD. (2020).
I have developed computational methods for analyzing empirical networks involving higher-order interactions among nodes (e.g., group conversation among three or more individuals).
- Inferring community structure in attributed hypergraphs using stochastic block models.
K. Nakajima and T. Uno. arXiv preprint. (2024). - Higher-order rich-club phenomenon in collaborative research grant networks.
K. Nakajima, K. Shudo, N. Masuda. Scientometrics. (2023). - Randomizing hypergraphs preserving degree correlation and local clustering.
K. Nakajima, K. Shudo, N. Masuda. IEEE Transactions on Network Science and Engineering. (2022).
I have quantitatively investigated structure, phenomena, and biases in the academic ecosystem (e.g., gender imbalance in academia and rich-club phenomenon in research funding) using a huge amount of bibliographic data.
- Quantifying gender imbalance in East Asian academia: Research career and citation practice.
K. Nakajima, R. Liu, K. Shudo, N. Masuda. Journal of Informetrics. (2023). - Higher-order rich-club phenomenon in collaborative research grant networks.
K. Nakajima, K. Shudo, N. Masuda. Scientometrics. (2023).
Publications
CV
Last update: 2024/03/01