Publications


Google Scholar Profile

Preprints

  1. Kazuki Nakajima, Takeaki Uno.
    Inferring community structure in attributed hypergraphs using stochastic block models.
    arXiv preprint arXiv:2401.00688 (2024).
    [arXiv] [code]

Refereed Journal Papers

  1. Kazuki Nakajima, Ruodan Liu, Kazuyuki Shudo, Naoki Masuda.
    Quantifying gender imbalance in East Asian academia: Research career and citation practice.
    Journal of Informetrics. Vol. 17, Article No. 101460 (2023).
    [paper] [arXiv] [プレスリリース（神戸大学）] [朝日新聞デジタル] [ITMedia NEWS]

  2. Kazuki Nakajima, Kazuyuki Shudo, Naoki Masuda.
    Higher-order rich-club phenomenon in collaborative research grant networks.
    Scientometrics. Vol. 128, pp. 2429–2446 (2023).
    [paper] [arXiv]

  3. Kazuki Nakajima, Kazuyuki Shudo.
    Random walk sampling in social networks involving private nodes.
    ACM Transactions on Knowledge Discovery from Data. Vol. 17, Article No. 51 (2022).
    [paper] [arXiv]

  4. Kazuki Nakajima, Kazuyuki Shudo, Naoki Masuda.
    Randomizing hypergraphs preserving degree correlation and local clustering.
    IEEE Transactions on Network Science and Engineering. Vol. 9, pp. 1139–1153 (2022).
    [paper] [arXiv] [code]

  5. Mei Fukuda, Kazuki Nakajima, Kazuyuki Shudo.
    Estimating the Bot Population on Twitter via Random Walk Based Sampling.
    IEEE Access. Vol. 10, pp. 17201–17211 (2022).
    [paper]

  6. Kazuki Nakajima, Kazuyuki Shudo.
    Measurement Error of Network Clustering Coefficients Under Randomly Missing Nodes.
    Scientific Reports. Vol. 11, Article No. 2815 (2021).
    [paper]

  7. Kazuki Nakajima, Kazuyuki Shudo.
    Estimating High Betweenness Centrality Nodes via Random walk in Social Networks.
    Journal of Information Processing. Vol. 28, pp. 436–444 (2020).
    [paper]

Refereed Conference Papers

  1. Yu Usui, Kazuki Nakajima, Chisa Takano, Masaki Aida.
    Perturbation Theory of Online User Dynamics with Respect to Change in Social Network Structures.
    Proceedings of the IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC 2023). pp. 0401-0407 (2023).

  2. Rikuya Miyashita, Kazuki Nakajima, Mei Fukuda, Kazuyuki Shudo.
    Random Hypergraph Model Preserving Two-Mode Clustering Coefficient.
    Proceedings of the 25th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2023). pp. 191–196 (2023).

  3. Kazuki Nakajima, Kazuyuki Shudo.
    Social Graph Restoration via Random Walk Sampling.
    Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE 2022). pp. 806–819 (2022).
    Full research paper. Out of 780 submissions, 27.1% of the papers were accepted.
    [paper] [arXiv] [code]

  4. Kazuki Nakajima, Kazuyuki Shudo.
    Estimating Properties of Social Networks via Random Walk considering Private Nodes.
    Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2020). pp. 720–730 (2020).
    Full research paper. Out of 1,279 submissions, 17.0% of the papers were accepted.
    [paper] [arXiv] [code]

  5. Kazuki Nakajima, Kenta Iwasaki, Toshiki Matsumura, Kazuyuki Shudo.
    Estimating Top-k Betweenness Centrality Nodes in Online Social Networks.
    Proceedings of the 11th IEEE International Conference on Social Computing and Networking (SocialCom 2018). pp. 1128–1135 (2018).
    Full research paper.
    [paper]

Conference/Workshop Presentations

  1. Kazuki Nakajima, Takeaki Uno.
    HyperNEO: Inference of community structure in attributed hypergraphs.
    NetSci 2024. June 2024.
    Oral presentation.

  2. Kazuki Nakajima, Takeaki Uno.
    HyperNEO: Inferring community structure in attributed hypergraphs.
    The 5th International Workshop on Machine Learning on Graphs (MLoG). March 2024.
    Poster presentation.

  3. Ryusei Yamamoto, Kazuki Nakajima, Masaki Aida.
    Exploring a model of interaction between structural change in online social networks and user dynamics.
    The 10th Anniversary Korea-Japan Joint Workshop on Complex Communication Sciences 2024 (KJCCS 2024). January 2024.
    Poster presentation.

  4. Takumi Sakiyama, Kazuki Nakajima, Masaki Aida.
    Intervention Strategies to Minimize the Spread of Misinformation.
    The 12th International Conference on Complex Networks and Their Applications. November 2023.
    Poster presentation.

  5. Ryusei Yamamoto, Kazuki Nakajima, Masaki Aida.
    Impact of Structural Changes in Networks induced by the Altered SIS Model on Online User Dynamics.
    The 12th International Conference on Complex Networks and Their Applications. November 2023.
    Poster presentation.

  6. Kazuki Nakajima, Ruodan Liu, Kazuyuki Shudo, Naoki Masuda.
    Quantitative analysis of gender imbalance in East Asian academia.
    The 2nd International Conference on the Science of Science and Innovation (ICSSI 2023). June 2023.
    Poster presentation.

  7. Rikuya Miyashita, Kazuki Nakajima, Mei Fukuda, Kazuyuki Shudo.
    Randomizing Hypergraphs Preserving Two-mode Clustering Coefficient.
    The 2023 IEEE International Conference on Big Data and Smart Computing (BigComp 2023). February 2023.
    Poster presentation.

  8. Kazuki Nakajima, Kazuyuki Shudo, Naoki Masuda.
    Random hypergraph models preserving degree correlation and local clustering.
    The 11th International Conference on Complex Networks and Their Applications. November 2022.
    Oral presentation.

  9. Kazuki Nakajima.
    Higher-order rich-club phenomenon in collaborative research grant networks.
    Socioeconomic networks and network science workshop 2022. August 2022.
    Oral presentation.

  10. Kazuki Nakajima, Kazuyuki Shudo, Naoki Masuda.
    Higher-order rich-club phenomenon in research funding.
    The fifth Northeast Regional Conference on Complex Systems (NERCCS 2022). March 2022.
    Oral presentation.

  11. Kazuki Nakajima, Kazuyuki Shudo, Naoki Masuda.
    Configuration models for hypergraphs preserving local quantities of nodes and hyperedges.
    The fourth Northeast Regional Conference on Complex Systems (NERCCS 2021). March 2021.
    Poster presentation.

  12. Mei Fukuda, Kazuki Nakajima, Kazuyuki Shudo.
    Comparison of Graph Generation Models focusing on Accuracy and Variation.
    The 16th International Workshop on Mining and Learning with Graphs (MLG 2020). August 2020.
    Oral presentation.

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Last update: 2023/03/01