Research

English / 日本語

Vast amounts of data on individuals’ connections, behaviors, and achievements provide valuable insights into society. However, such social data are often not well-suited for research purposes due to limitations in accessibility, increasing structural complexity, and domain-specific biases.

My research focuses on developing computational and mathematical methods to address these practical challenges in social data analysis, and on applying these methods to empirical data in order to deepen our understanding of the structure and dynamics of society.

My primary research interests include:

• Complex Systems
• Computational Social Science
• Network Science
• Science of Science

My research style emphasizes steady progress through extensive and in-depth discussions, with the goal of producing carefully constructed and reliable research outcomes. I welcome inquiries regarding my research, academic discussions, and potential collaborations (with some exceptions; due to my current position, I am unable to provide formal research supervision). I have particular strengths in data construction and curation, methodological development, and large-scale computational analysis. I am also actively developing my expertise in theoretical modeling and hypothesis-driven research from a social science perspective. If you are interested in any of the areas listed above, there may be opportunities to explore collaboration.

My research projects

Here are some of my projects.

1. Development of computational methods for higher-order social networks.

• Learning Multi-Order Block Structure in Higher-Order Networks.
  Kazuki Nakajima, Yuya Sasaki, Takeaki Uno, and Masaki Aida. arXiv. (2025).
• Sampling nodes and hyperedges via random walks on large hypergraphs.
  Kazuki Nakajima, Masanao Kodakari, and Masaki Aida. Applied Network Science. (2025).
• Inference and Visualization of Community Structure in Attributed Hypergraphs Using Mixed-Membership Stochastic Block Models.
  Kazuki Nakajima and Takeaki Uno. Social Network Analysis and Mining. (2025).
• Higher-order rich-club phenomenon in collaborative research grant networks.
  Kazuki Nakajima, Kazuyuki Shudo, and Naoki Masuda. Scientometrics. (2023).
• Randomizing hypergraphs preserving degree correlation and local clustering.
  Kazuki Nakajima, Kazuyuki Shudo, and Naoki Masuda. IEEE Transactions on Network Science and Engineering. (2022).

2. Data analysis of structure and dynamics in the scientific ecosystem.

• Quantifying gendered citation imbalance in computer science conferences.
  Kazuki Nakajima, Yuya Sasaki, Sohei Tokuno, and George Fletcher. AIES 2024. (2024).
• Quantifying gender imbalance in East Asian academia: Research career and citation practice.
  Kazuki Nakajima, Ruodan Liu, Kazuyuki Shudo, and Naoki Masuda. Journal of Informetrics. (2023).
• Higher-order rich-club phenomenon in collaborative research grant networks.
  Kazuki Nakajima, Kazuyuki Shudo, and Naoki Masuda. Scientometrics. (2023).

3. Development of computational methods for large-scale social networks.

• Researcher Population Pyramids: Tracking Demographic and Gender Trajectories Across Countries.
  Kazuki Nakajima and Takayuki Mizuno. arXiv preprint (2025).
• Social Graph Restoration via Random Walk Sampling.
  Kazuki Nakajima and Kazuyuki Shudo. Proc. ICDE. (2022).
• Estimating the Bot Population on Twitter via Random Walk Based Sampling.
  Mei Fukuda, Kazuki Nakajima, and Kazuyuki Shudo. IEEE Access. (2022).
• Estimating Properties of Social Networks via Random Walk considering Private Nodes.
  Kazuki Nakajima and Kazuyuki Shudo. Proc. KDD. (2020).

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