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Machine Learning and Knowledge Engineering for Decision Making

The 17th International FLINS Conference on Fuzzy Logic for Intelligent Systems and The 21st International Conference on Intelligent Systems and Knowledge Engineering, FLINS-ISKE 2026, Sydney, NSW, Australia, July 15–19, 2026, Proceedings, Part IV

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  • © 2027
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Overview

Editors:
  1. Jie Lu

    1. University of Technology Sydney, Sydney, Australia

  2. Javier Montero

    1. Universidad Complutense de Madrid, Madrid, Spain

  3. Yi Zhang

    1. University of Technology Sydney, Sydney, Australia

  4. Junyu Xuan

    1. University of Technology Sydney, Sydney, Australia

  5. Tianrui Li

    1. Southwest Jiaotong University, Chengdu, China

  6. Luis Martínez

    1. University of Jaén, Jaén, Spain

  7. Etienne Kerre

    1. Ghent University, Ghent, Belgium

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 16755)

Included in the following conference series:

Conference proceedings info: FLINS-ISKE 2026.

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About this book

This book constitutes the refereed proceedings of the 17th Conference on Fuzzy Logic and Intelligent Technologies in Nuclear Science, FLINS 2026, and 21st International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2026, held in Sydney, NSW, Australia, during July 15–19, 2026.

The 152 full papers and 8 short papers presented in this book were carefully selected and reviewed from 275 submissions. These papers have been organized in the following topical sections:

Part I: Fuzzy Machine Learning and Decision Making

Part II: Knowledge Engineering, LLMs, and Reasoning

Part III: Computer Vision and Medical Imaging

Part IV: Graph Neural Networks and Relational Intelligence

Part V: Trustworthy, Federated, and Robust Learning Systems

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Table of contents (25 papers)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Graph Neural Networks and Relational Intelligence

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Self-attentive Sequential Recommendation with User Preference Clustering

      • Dang Huu Trong Ho, Thi Thanh Sang Nguyen
      Pages 3-14

    3. TGNN: A Teacher-Guided Graph Neural Network Framework for Semi-supervised Learning on Non-stationary Streaming Data

      • Shukun Li, Hang Yu, Yimin Wen, Huafu Xu
      Pages 15-36

    4. A Semi-supervised Approach with Dual Filters for Graph-Based Fraud Detection

      • Lijing Huang, Zhengyang Liu, Xiangfeng Luo, Shaorong Xie
      Pages 37-50

    5. CEGNN: Cross-Environment Consistent Graph Neural Networks for Robust OOD Generalization

      • Qiying Li, Tingyi Cai, Ruiting Zhao, Xinyi Li, Ming Li
      Pages 51-71

    6. Feature Fusion-Based Attribute Graph Clustering

      • Jingxiao Zhang, Chao Li, Zhenghua Xin, Shifei Ding
      Pages 72-87

    7. A Deep Non-negative Matrix Factorization Algorithm for Acupuncture Omics Data Clustering

      • Zonglin Li, Lianbo Tao, Qiaofeng Wu, Ping Deng, Pengfei Zhang, Dexian Wang et al.
      Pages 88-104

    8. Prediction of Forest-Grassland Fires Based on Graph Attention Mechanism

      • Xingpeng Liu, Sen Wang, Rima Ga, Bing Ma, Jiquan Zhang, Zhijun Tong
      Pages 105-118

    9. Decimated Hypergraph Framelets Based on Spline Wavelets Basis

      • Chenghao Qiu, Ming Li, Xiaosheng Zhuang
      Pages 119-136

    10. SPARQ: Graph-Structured Queries Drive GNN Retrieval for Knowledge Graph Question Answering

      • Mingxuan Wang, Xinzhi Wang, Ruishen Liu, Yuxuan Li, Xiangfeng Luo
      Pages 137-150

    11. TrustGIM: Trust-aware Core Graph Influence Maximization

      • Zhixiang Xu, Kai Luo, Yuyao Meng, Yunliang Chen, Xiaohui Huang, Yuewei Wang et al.
      Pages 151-183

    12. ProbSparseAtt-Score Based Sparse Scene Graph Generation Model

      • Yuyao Meng, Kai Luo, Zhixiang Xu, Yunliang Chen, Xiaohui Huang, Yuewei Wang et al.
      Pages 184-205

    13. TGCRN: Time-Varying Graph Convolutional Recurrent Network for Traffic Flow Prediction

      • Yaxi Pan, Jingbo Hou, Wei Huang, Jihong Wan, Jie Wang, Xiaocao Ouyang et al.
      Pages 206-226

    14. LLM-Enhanced Multi-granularity Hierarchical Perception in Temporal Knowledge Graph Completion

      • Chaohong Wu, Siling Feng, Shan Xue, Liqi Wang, Weigang Kong
      Pages 227-248

    15. HGCRE: Heterogeneous Graph Representation Learning via Contrastive Learning

      • Dongming Chen, Hongxun Wang, Liu Jiaming, Beilei Wang, Tao Ren
      Pages 249-264

    16. A Global Path-Aware Graph Neural Network for Link Prediction

      • Dongming Chen, Shuyue Zhang, Yue Shen, Beilei Wang, Dongqi Wang
      Pages 265-290

    17. Re-KGR: Mitigating Factual Hallucinations via Hybrid Graph Reasoning and Semantic Refinement

      • Yuxuan Li, Xinzhi Wang, Ruishen Liu, Mingxuan Wang, Xiangfeng Luo
      Pages 291-305

    18. Score: Bursting Dual Heterogeneity with a Semantic-Collaborative Graph Attention Framework for Cross-Domain Sequential Recommendation

      • Yuxi Dong, Xiongtao Zhang
      Pages 306-325

    19. Interest-Expansion-Based Graph Convolutional Neural Networks for Recommendation

      • Wenhui Guo, Tiantian Zhou, Sihui Li, Hailiang Ye, Feilong Cao
      Pages 326-346
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Other volumes

  1. Machine Learning and Knowledge Engineering for Decision Making

  2. Machine Learning and Knowledge Engineering for Decision Making

  3. Machine Learning and Knowledge Engineering for Decision Making

  4. Machine Learning and Knowledge Engineering for Decision Making

  5. Machine Learning and Knowledge Engineering for Decision Making

Editors and Affiliations

  • University of Technology Sydney, Sydney, Australia

    Jie Lu, Yi Zhang, Junyu Xuan

  • Universidad Complutense de Madrid, Madrid, Spain

    Javier Montero

  • Southwest Jiaotong University, Chengdu, China

    Tianrui Li

  • University of Jaén, Jaén, Spain

    Luis Martínez

  • Ghent University, Ghent, Belgium

    Etienne Kerre

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Bibliographic Information

  • Book Title: Machine Learning and Knowledge Engineering for Decision Making

  • Book Subtitle: The 17th International FLINS Conference on Fuzzy Logic for Intelligent Systems and The 21st International Conference on Intelligent Systems and Knowledge Engineering, FLINS-ISKE 2026, Sydney, NSW, Australia, July 15–19, 2026, Proceedings, Part IV

  • Editors: Jie Lu, Javier Montero, Yi Zhang, Junyu Xuan, Tianrui Li, Luis Martínez, Etienne Kerre

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-981-92-2494-4

  • Publisher: Springer Singapore

  • eBook Packages: Computer Science, Computer Science (R0), Springer Nature Proceedings Computer Science

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2027

  • Softcover ISBN: 978-981-92-2493-7Published: 09 July 2026

  • eBook ISBN: 978-981-92-2494-4Published: 08 July 2026

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XIII, 472

  • Number of Illustrations: 2 b/w illustrations, 106 illustrations in colour

  • Topics: Machine Learning, Artificial Intelligence, Algorithm Analysis and Problem Complexity, Data Mining and Knowledge Discovery

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