About Me

Shu Liu is currently working as a Data Scientist at Japan Data Science Consortium Co. Ltd..
Previously, he was a Postdoctoral (PD) Research Fellow of the Japan Society for the Promotion of Science (JSPS) in Toriumi Lab at the Graduate School of Engineering, The University of Tokyo.
His research background is structural information mining in Complex Networks.
He focused on developing structure-based embedding methods for various types of networks, including signed networks, directed networks, weighted networks, and hypergraphs.
He is interested in applying these methods to practical applications, such as genomics, biomechanics, and society.
He currently serves on the steering committee for the Network Science Study Group

Career

2026.1-present, Data Scientist at Japan Data Science Consortium Co. Ltd..
2024.10-2025.12, JSPS research fellow (PD).
2024.4-2024.9, JSPS research fellow (DC2).
2023.9-2024.2, Visiting PGR Student at Department of Computer Science, University of Manchester, supervised by Ass. Prof. Tingting Mu.
2021-2024, Ph.D. majoring Systems Innovation at the Graduate School of Engineering, The University of Tokyo, supervised by Prof. Fujio Toriumi.
2021-2024.3, JST Support for Pioneering Research Initiated by the Next Generation (SPRING) Program, The University of Tokyo "Advanced Human Resource Development Leading Green Transformation (GX) (SPRING GX)" Project student.
2021-2024.3, Fellowship for Creation of Intelligent World, International Graduate Program of Innovation for Intelligent World, The University of Tokyo.
2019-2021, MEng majoring Systems Innovation at the Graduate School of Engineering, The University of Tokyo, supervised by Prof. Fujio Toriumi.
2013-2019, System engineering for Department of train control systems in Hitachi, Ltd., etc..
2007-2012, BEng majoring Mechanical Design & Manufacturing and Its Automation (Intensive Japanese) at School of Mechanical Engineering, Dalian University of Technology.

Publications

Refereed journal papers and preprints

• HyperS2V: A Framework for Structural Representation of Nodes in Hyper Networks
  Shu Liu, Cameron Lai, Masaki Chujyo, Fujio Toriumi
  Appl Netw Sci (2026). https://doi.org/10.1007/s41109-025-00771-2
  Code


• Steering polarization toward consensus in signed majority-vote opinion models
  Masaki Chujyo, Shu Liu, Fujio Toriumi
  Scientific Reports 15, 39882 (2025). https://doi.org/10.1038/s41598-025-23601-y
  


• SDW2vec: Learning Structural Representations of Nodes in Weighted Networks
  Shu Liu, Masaki Chujyo, Fujio Toriumi
  Appl Netw Sci 10, 32 (2025). https://doi.org/10.1007/s41109-025-00722-x
  Code


• Improving network robustness against localized attacks by link modification
  Masaki Chujyo, Shu Liu, Fujio Toriumi
  Nonlinear Theory and Its Applications, IEICE, Volume 16 Issue 3 Pages 561-572. https://doi.org/10.1587/nolta.16.561
  


• Hypergraph modeling of complex interactions: Applications from human musculoskeletal structures to complex system dynamics
  Hiroko Yamano, Shu Liu, Fujio Toriumi
  PLoS ONE 19(11): e0310189. https://doi.org/10.1371/journal.pone.0310189
  


• (Preprint) Gene2role: a role-based gene embedding method for comparative analysis of signed gene regulatory networks
  Xin Zeng, Shu Liu, Bowen Liu, Weihang Zhang, Wanzhe Xu, Fujio Toriumi, Kenta Nakai
  BMC Bioinformatics 26, 134 (2025). https://doi.org/10.1186/s12859-025-06128-x
  Code


• (Preprint) SDs2vec: Structural embedding method for Signed Directed network
  Shu Liu, Fujio Toriumi, Masaki Chujyo
  Code


• A flexible framework for multiple-role discovery in real networks
  Shu Liu, Fujio Toriumi, Mao Nishiguchi, Shohei Usui
  Appl Netw Sci 7, 67 (2022). https://doi.org/10.1007/s41109-022-00509-4
  Code


• Framework for role discovery using transfer learning
  Shumpei Kikuta, Fujio Toriumi, Mao Nishiguchi, Shu Liu, Tomoki Fukuma, Takanori Nishida, Shohei Usui
  Appl Netw Sci 5, 45 (2020). https://doi.org/10.1007/s41109-020-00281-3
  

Refereed conference papers

• SignedS2V: Structural Embedding Method for Signed Networks
  Shu Liu, Fujio Toriumi, Xin Zeng, Mao Nishiguchi, Kenta Nakai
  Complex Networks and Their Applications XI. COMPLEX NETWORKS 2022. Studies in Computational Intelligence, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-21127-0_28
  Code


• Multiple role discovery in complex networks
  Shu Liu, Fujio Toriumi, Mao Nishiguchi, Shohei Usui
  Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1016. Springer, Cham. https://doi.org/10.1007/978-3-030-93413-2_35

Conferences

International Conferences

• Shu Liu, Fujio Toriumi; Visualization of Sport Climbing Routes: An Approach Using Directed Network Analysis; NetSci 2025; 410; Maastricht, the Netherlands; June 2025.
• Shu Liu, Masaki Chujyo, Fujio Toriumi; Learning Structural Representations of Nodes in Weighted Networks; COMPLEX NETWORKS 2024; 57; Istanbul, Turkey; December 2024.
• Shu Liu, Xin Zeng, Kenta Nakai, Fujio Toriumi; A Roles-based Approach for Characterizing Signed Gene Regulatory Networks; NetSci 2024; 001; Québec City, Canada; June 2024.
• Shu Liu, Cammeron Lai, Fujio Toriumi; A Framework for Structural Representation of Nodes in Hyper Networks; COMPLEX NETWORKS 2023; 56; Menton Riviera, France; November 2023.
• Shu Liu and Fujio Toriumi; A Framework for Structural Representation of Nodes in Signed Directed Networks; NetSci 2023; 121; Vienna, Austria; July 2023.
• Shu Liu, Fujio Toriumi, Xin Zeng, Mao Nishiguchi, Kenta Nakai; SignedS2V: Structural Embedding Method for Signed Networks; COMPLEX NETWORKS 2022; 41; Palermo, Italy; November 2022.
• Shu Liu, Fujio Toriumi, Mao Nishiguchi, Shohei Usui; Multiple Role Discovery in Complex Networks; COMPLEX NETWORKS 2021; 116; Madrid, Spain; November 2021.
• Shu Liu, Fujio Toriumi, Mao Nishiguchi, Shohei Usui; Predicting multiple roles of nodes in complex network using domain adversarial learning; Networks 2021; 10171; VIRTUAL; July 2021.

Domestic Conferences

• 劉 庶，中条雅貴，鳥海 不二夫; スポーツクライミングルートとクライマー動作の可視化：有向ネットワーク分析を用いたアプローチ; JSAI2025; 1Q4-GS-10-05； 大阪; 5月，2025年．
• 劉 庶，中条雅貴，鳥海不二夫; ネットワーク成長とノード埋め込みの形成; NetEco2025; P11； 金沢; 3月，2025年．
• 劉 庶，Cameron Lai，中条雅貴，鳥海不二夫; ハイパーグラフの構造的埋め込みによるソーシャルネットワーク分析; CSSJ2025; P2-19； 東京; 2月，2025年．
• 劉 庶，鳥海 不二夫; 重み付きネットワークにおけるノードの構造的埋め込み表現; JSAI2024; 1B4-GS-2-05； 浜松; 5月，2024年．
• 劉 庶，鳥海 不二夫; ネットワークの構造における重みの扱いと分散表現学習; NetEco19; 11; 長崎; 2月，2024年．
• 劉 庶，鳥海 不二夫，田島浩幸; スキルの需要と供給の可視化：符号付きネットワーク分析によるアプローチ; JSAI2023; 2L5-GS-3； 熊本; 6月，2023年．
• 劉 庶，鳥海 不二夫，Cameron Lai; ハイパーネットワークにおける構造的埋め込み表現取得; NetEco18; 2; 北海道; 3月，2023年．
• 劉 庶，鳥海 不二夫; 実社会の大規模データにおける符号付ネットワークの構造的埋め込み手法の提案; WSSIT2023; JSSST SIG-EIN05; 北海道; 3月，2023年．
• 劉 庶，鳥海 不二夫; リンクの埋め込み表現学習; JSAI2022; 4E3-GS-2-02 京都; 6月，2022年．
• 劉 庶，鳥海 不二夫；実ネットワークにおける構造的マルチラベルのマイニング；ネットワーク生態学シンポジウム；P7；オンライン；3月，2022年．
• 劉 庶，鳥海 不二夫；実ネットワークにおける役割情報のマルチラベル分類；社会情報システム学シンポジウム；3-4；沖縄；1月，2022年．
• 劉 庶，鳥海 不二夫；ノードの複数役割発見のための転移学習；第27回社会情報システム学シンポジウム；1-1；オンライン；1月，2021年.
• 劉 庶，菊田 俊平，鳥海 不二夫；転移学習に用いたリンクの役割発見；人工知能学会全国大会；2E6-GS-5-03；オンライン；6月，2020年.

Activities

• 東京大学　システム創成優秀博士学生賞
• Student Incentive Award at JSAI Annual Conference 2024
• Student Volunteer at COMPLEX NETWORKS 2023
• Student Incentive Award at JSAI Annual Conference 2023
• (Invited lecture) 劉 庶；今時のネットワーク科学のトレンドは？～ComplexNetworks2022国際会議参加報告～；第18回 ネットワーク生態学シンポジウム；北海道；3月，2023年．