Research

English / 日本語

Vast amounts of data on individuals' connections, behaviors, and achievements offer valuable insights into society. However, these social data are often suboptimal for research due to limitations in accessibility and domain-specific biases. I focus on developing computational methods to address these practical challenges in social data analysis and applying these methods to empirical data to deepen our understanding of the structure and dynamics of society.

My areas of interest include (Will expand in the future):

• Network Science
• Science of Science

My love research style is to steadily build a fine piece of work through many, many in-depth discussions. I welcome inquiries about my research, discussions, and potential for collaboration (except in a few cases). I am good at data construction, curation, and precise calculations. I am not yet very good at making good research hypotheses on our society. I am still training. If you are interested in either of the above research areas, we might be able to explore the potential for collaboration.

My research projects

Here are some of my projects.

1. Development of computational methods for analyzing social networks involving higher-order interactions among individuals.

• Sampling nodes and hyperedges via random walks on large hypergraphs.
  Kazuki Nakajima, Masanao Kodakari, and Masaki Aida. arXiv. (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. Bibliographic data analysis of structure, phenomena, and imbalances/biases in the academic 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 estimating structural properties of large-scale social networks consisting of friendships between individuals.

• 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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