Kazuki Nakajima (中嶋 一貴)

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

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News

• (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) I will deliver a presentation at WAW 2025.
  Hypergraph sampling via random walk and its application to OpenAlex database.
  Kazuki Nakajima, Masanao Kodakari, and Masaki Aida.


• (2025/01) I will deliver an oral presentation at NetSci2025.
  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.


• (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.


• (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.


• (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]

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