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Database Systems for Advanced Applications

29th International Conference, DASFAA 2024, Gifu, Japan, July 2–5, 2024, Proceedings, Part VI

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Makoto Onizuka

    1. Osaka University, Suita, Japan

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  2. Jae-Gil Lee

    1. KAIST, Daejeon, Korea (Republic of)

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  3. Yongxin Tong

    1. Beihang University, Beijing, China

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  4. Chuan Xiao

    1. Osaka University, Osaka, Japan

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  5. Yoshiharu Ishikawa

    1. Nagoya University, Nagoya, Japan

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  6. Sihem Amer-Yahia

    1. University of Grenoble Alpes, Saint-Martin d’Hères, France

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  7. H. V. Jagadish

    1. University of Michigan, Ann Arbor, USA

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  8. Kejing Lu

    1. Nagoya University, Nagoya, Japan

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14855)

Included in the following conference series:

Conference proceedings info: DASFAA 2024.

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About this book

The seven-volume set LNCS 14850-14856 constitutes the proceedings of the 29th International Conference on Database Systems for Advanced Applications, DASFAA 2024, held in Gifu, Japan, in July 2024.

The total of 147 full papers, along with 85 short papers, presented together in this seven-volume set was carefully reviewed and selected from 722 submissions.

Additionally, 14 industrial papers, 18 demo papers and 6 tutorials are included.

The conference presents papers on subjects such as:

Part I: Spatial and temporal data; database core technology; federated learning.

Part II: Machine learning; text processing.

Part III: Recommendation; multi-media.

Part IV: Privacy and security; knowledge base and graphs.

Part V: Natural language processing; large language model; time series and stream data.

Part VI: Graph and network; hardware acceleration.

Part VII: Emerging application; industry papers; demo papers.

Keywords

Table of contents (35 papers)

  1. Front Matter

    Pages i-xxvi
    Download chapter PDF

  2. Graph and Network

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Cascading Graph Convolution Contrastive Learning Networks for Multi-behavior Recommendation

      • Nan Liu, Shunmei Meng, Yu Jiang, Qianmu Li, Xiaolong Xu, Lianyong Qi et al.
      Pages 3-18

    3. Social Relation Enhanced Heterogeneous Graph Contrastive Learning for Recommendation

      • Jiaxi Wang, Bingce Wang, Liwen Zhang, Tong Mo, Weiping Li
      Pages 19-34

    4. Higher-Order Graph Contrastive Learning for Recommendation

      • ZhenZhong Zheng, Jianxin Li, Xiaoming Wu, Xiangzhi Liu, Lili Pei
      Pages 35-51

    5. FNDPro: Evaluating the Importance of Propagations during Fake News Spread

      • Herun Wan, Ningnan Wang, Xiang Zhao, Rui Li, Hui Yang, Minnan Luo
      Pages 52-67

    6. Leveraging Homophily-Augmented Energy Propagation for Bot Detection on Graphs

      • Bradley Ashmore, Lingwei Chen
      Pages 68-83

    7. Multi-level Contrastive Learning on Weak Social Networks for Information Diffusion Prediction

      • Zihan Feng, Rui Wu, Yajun Yang, Hong Gao, Xin Wang, Xueli Liu et al.
      Pages 84-100

    8. BiasRec: A General Bias-Aware Social Recommendation Model

      • Chunkai Zhang, Guoqing Li
      Pages 101-116

    9. Beyond the Known: Novel Class Discovery for Open-World Graph Learning

      • Yucheng Jin, Yun Xiong, Juncheng Fang, Xixi Wu, Dongxiao He, Xing Jia et al.
      Pages 117-133

    10. Robust Graph Recommendation via Noise-Aware Adversarial Perturbation

      • Jiakai Tang, Zuxu Sun, Wei Yao, Xu Chen
      Pages 134-150

    11. Learning Social Graph for Inactive User Recommendation

      • Nian Liu, Shen Fan, Ting Bai, Peng Wang, Mingwei Sun, Yanhu Mo et al.
      Pages 151-167

    12. MANE: A Multi-cascade Adversarial Network Embedding Model for Anchor Link Prediction

      • Jingya Zhou
      Pages 168-184

    13. uTransfer: Unified Transferability Metric Incorporating Heterogeneous User Data in Social Network

      • Nuo Li, Bin Guo, Yao Jing, Zhiwen Yu
      Pages 185-202

    14. GPSR: Graph Prompt for Session-Based Recommendation

      • Cheng Li, Pei-Yuan Lai, Yi-Hong Lu, De-Zhang Liao, Xiao-Dong Huang, Chang-Dong Wang
      Pages 203-219

    15. Guiding Graph Learning with Denoised Modality for Multi-modal Recommendation

      • Yuexian Wang, Wenze Ma, Yanmin Zhu, Chunyang Wang, Zhaobo Wang, Feilong Tang et al.
      Pages 220-235

    16. Enhancing Multi-view Contrastive Learning for Graph Anomaly Detection

      • Qingcheng Lu, Nannan Wu, Yiming Zhao, Wenjun Wang, Quannan Zu
      Pages 236-251

    17. Global Route Planning for Large-Scale Requests on Traffic-Aware Road Network

      • JiaJia Li, Yannan Wang, Rui Zhu, Jing Zhang, Na Guo
      Pages 252-268

    18. TransGAD: A Transformer-Based Autoencoder for Graph Anomaly Detection

      • Zehao Guo, Nannan Wu, Yiming Zhao, Wenjun Wang
      Pages 269-284

    19. Unsupervised Node Clustering via Contrastive Hard Sampling

      • Hang Cui, Tarek Abdelzaher
      Pages 285-300
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Other volumes

  1. Database Systems for Advanced Applications

  2. Database Systems for Advanced Applications

  3. Database Systems for Advanced Applications

  4. Database Systems for Advanced Applications

  5. Database Systems for Advanced Applications

Editors and Affiliations

  • Osaka University, Suita, Japan

    Makoto Onizuka

  • KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  • Beihang University, Beijing, China

    Yongxin Tong

  • Osaka University, Osaka, Japan

    Chuan Xiao

  • Nagoya University, Nagoya, Japan

    Yoshiharu Ishikawa, Kejing Lu

  • University of Grenoble Alpes, Saint-Martin d’Hères, France

    Sihem Amer-Yahia

  • University of Michigan, Ann Arbor, USA

    H. V. Jagadish

Bibliographic Information

  • Book Title: Database Systems for Advanced Applications

  • Book Subtitle: 29th International Conference, DASFAA 2024, Gifu, Japan, July 2–5, 2024, Proceedings, Part VI

  • Editors: Makoto Onizuka, Jae-Gil Lee, Yongxin Tong, Chuan Xiao, Yoshiharu Ishikawa, Sihem Amer-Yahia, H. V. Jagadish, … Kejing Lu

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-981-97-5572-1

  • Publisher: Springer Singapore

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

  • Softcover ISBN: 978-981-97-5571-4Published: 01 September 2024

  • eBook ISBN: 978-981-97-5572-1Published: 30 August 2024

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXVI, 487

  • Number of Illustrations: 4 b/w illustrations, 132 illustrations in colour

  • Topics: Machine Learning, Computer Applications, Computing Milieux, Computer Communication Networks, Special Purpose and Application-Based Systems

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