Research

A vast amount of social data on individuals' connections and behavior provides valuable insights into the structure and dynamics of society. However, such data is often suboptimal for research purposes due to limitations in accessibility or inherent social biases. I am focusing on developing computational methods to effectively analyze and address these challenges in social data analyses.

My areas of interest include:

• Computational Social Science
• Network Science
• Science of Science

I have developed computational methods for estimating structural properties of large-scale online social networks composed of friendships between numerous users on social media platforms.

• 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, Kazuyuki Shudo. IEEE Access. (2022).
• Estimating Properties of Social Networks via Random Walk considering Private Nodes.
  Kazuki Nakajima and Kazuyuki Shudo. Proc. KDD. (2020).

I have developed computational methods for analyzing empirical networks involving higher-order interactions among individuals (e.g., group conversation among three or more individuals).

• Inferring community structure in attributed hypergraphs using stochastic block models.
  Kazuki Nakajima and Takeaki Uno. arXiv preprint. (2024).
• Higher-order rich-club phenomenon in collaborative research grant networks.
  Kazuki Nakajima, Kazuyuki Shudo, Naoki Masuda. Scientometrics. (2023).
• Randomizing hypergraphs preserving degree correlation and local clustering.
  Kazuki Nakajima, Kazuyuki Shudo, Naoki Masuda. IEEE Transactions on Network Science and Engineering. (2022).

I have quantitatively investigated structure, phenomena, and biases in the academic ecosystem using a huge amount of bibliographic data.

• 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, Naoki Masuda. Journal of Informetrics. (2023).
• Higher-order rich-club phenomenon in collaborative research grant networks.
  Kazuki Nakajima, Kazuyuki Shudo, Naoki Masuda. Scientometrics. (2023).

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