Kazuki Nakajima

Kazuki Nakajima (中嶋 一貴)

Assistant Professor
Graduate School of Systems Design
Tokyo Metropolitan University, Japan

Email: nakajima [at] tmu.ac.jp
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About

I am interested in complex systems involving humans. My research aims to accurately understand the structure and dynamics of society from two perspectives: statistical analysis of complex networks and metascience based on large-scale bibliographic data. See the Research page for details.

I received my Ph.D. (Doctor of Science) from Tokyo Institute of Technology in September 2022. I was then a JSPS Research Fellow (PD) and a postdoctoral researcher at the University at Buffalo, State University of New York, until March 2023, and have been in my current position since April 2023.

Keywords: network science, complex networks, social networks, metascience, science of science

News

Older news
  • (2025/07) Oral presentation at CCS 2025.
    Projecting Global Shifts in Scholarly Maturity and Gender Balance in Academia.
    Kazuki Nakajima and Takayuki Mizuno.

  • (2025/07) New preprint is on arXiv.
    Researcher Population Pyramids for Tracking Global Demographic and Gender Trajectories.
    Kazuki Nakajima and Takayuki Mizuno.

  • (2025/04) Our paper has been accepted for Applied Network Science.
    Sampling nodes and hyperedges via random walks on large hypergraphs.
    Kazuki Nakajima, Masanao Kodakari, and Masaki Aida.

  • (2025/03) Oral presentation at WAW 2025.
    Hypergraph sampling via random walk and its application to OpenAlex database.
    Kazuki Nakajima, Masanao Kodakari, and Masaki Aida.

  • (2025/01) Oral presentation at NetSci 2025.
    Quantitative analysis of citation imbalance in computer science using reference models.
    Kazuki Nakajima, Yuya Sasaki, Sohei Tokuno, and George Fletcher.

  • (2025/01) Our paper has been accepted for Social Network Analysis and Mining.
    Inference and Visualization of Community Structure in Attributed Hypergraphs Using Mixed-Membership Stochastic Block Models.
    Kazuki Nakajima and Takeaki Uno.
    [paper]

  • (2024/12) Delivered an oral presentation at Complex Networks 2024.
    • Inference and visualization of community structure in grant collaboration hypergraphs.
      Kazuki Nakajima and Takeaki Uno.

  • (2024/10) Delivered a poster presentation at AIES 2024.
    Quantifying gendered citation imbalance in computer science conferences.
    Kazuki Nakajima, Yuya Sasaki, Sohei Tokuno, and George Fletcher.

  • (2024/10) Our two conference papers have been accepted for Complex Networks 2024.
    • Inference and visualization of community structure in grant collaboration hypergraphs.
      Kazuki Nakajima and Takeaki Uno.
    • Estimating hyperedge size distribution via random walk on hypergraphs.
      Masanao Kodakari, Kazuki Nakajima, and Masaki Aida.

  • (2024/10) Implemented Python code with numba for hyper dK-series, which is 20-30 times faster than the previous version for the parameter d_v = 2 and 2.5 and achieves comparable generation time for C++ code. Randomized instances generated by the hyper dK-series in Python can be converted to the Hypergraph object of HyperNetX.

  • (2024/09) Our paper has been accepted for IEEE Access.
    Efficient intervention in the spread of misinformation in social networks.
    Takumi Sakiyama, Kazuki Nakajima, and Masaki Aida.
    [paper]

  • (2024/09) Delivered a poster presentation at CCS 2024.
    Non-strong community structure in collaborative research grant hypergraphs.
    Kazuki Nakajima and Takeaki Uno.

  • (2024/08) Our new paper is on arXiv.
    Quantifying gendered citation imbalance in computer science conferences.
    Kazuki Nakajima, Yuya Sasaki, Sohei Tokuno, and George Fletcher.

  • (2024/07) Our paper has been accepted for AIES 2024.
    Quantifying gendered citation imbalance in computer science conferences.
    Kazuki Nakajima, Yuya Sasaki, Sohei Tokuno, and George Fletcher.
    [paper]

  • (2024/07) Delivered a poster presentation at ICSSI 2024.
    Quantifying citation imbalance in computer science: Gender and conference tier.
    Kazuki Nakajima and Yuya Sasaki.

  • (2024/06) Delivered an oral presentation on the latest work at NetSci 2024.
    HyperNEO: Inference of community structure in attributed hypergraphs.
    Kazuki Nakajima and Takeaki Uno. (2024). [arXiv]