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Advances in Knowledge Discovery and Data Mining

25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part III

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Kamal Karlapalem

    1. IIIT, Hyderabad, Hyderabad, India

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  2. Hong Cheng

    1. Chinese University of Hong Kong, Shatin, Hong Kong

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  3. Naren Ramakrishnan

    1. Virginia Tech, Arlington, USA

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  4. R. K. Agrawal

    1. Jawaharlal Nehru University, New Delhi, India

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  5. P. Krishna Reddy

    1. IIIT Hyderabad, Hyderabad, India

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  6. Jaideep Srivastava

    1. University of Minnesota, Minneapolis, USA

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  7. Tanmoy Chakraborty

    1. IIIT Delhi, New Delhi, India

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12714)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: PAKDD 2021.

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Table of contents (34 papers)

  1. Front Matter

    Pages i-xxiii
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  2. Representation Learning and Embedding

    1. Front Matter

      Pages 1-1
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    2. Episode Adaptive Embedding Networks for Few-Shot Learning

      • Fangbing Liu, Qing Wang
      Pages 3-15

    3. Universal Representation for Code

      • Linfeng Liu, Hoan Nguyen, George Karypis, Srinivasan Sengamedu
      Pages 16-28

    4. Self-supervised Adaptive Aggregator Learning on Graph

      • Bei Lin, Binli Luo, Jiaojiao He, Ning Gui
      Pages 29-41

    5. A Fast Algorithm for Simultaneous Sparse Approximation

      • Guihong Wan, Haim Schweitzer
      Pages 42-54

    6. STEPs-RL: Speech-Text Entanglement for Phonetically Sound Representation Learning

      • Prakamya Mishra
      Pages 55-66

    7. RW-GCN: Training Graph Convolution Networks with Biased Random Walk for Semi-supervised Classification

      • Yinzhe Li, Zhijie Ban
      Pages 67-76

    8. Loss-Aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models

      • Mojtaba Nayyeri, Chengjin Xu, Yadollah Yaghoobzadeh, Sahar Vahdati, Mirza Mohtashim Alam, Hamed Shariat Yazdi et al.
      Pages 77-89

    9. SST-GNN: Simplified Spatio-Temporal Traffic Forecasting Model Using Graph Neural Network

      • Amit Roy, Kashob Kumar Roy, Amin Ahsan Ali, M. Ashraful Amin, A. K. M. Mahbubur Rahman
      Pages 90-102

    10. VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning

      • Viresh Gupta, Tanmoy Chakraborty
      Pages 103-115

    11. Self-supervised Graph Representation Learning with Variational Inference

      • Zihan Liao, Wenxin Liang, Han Liu, Jie Mu, Xianchao Zhang
      Pages 116-127

    12. Manifold Approximation and Projection by Maximizing Graph Information

      • Bahareh Fatemi, Soheila Molaei, Hadi Zare, Shirui Pan
      Pages 128-140

    13. Learning Attention-Based Translational Knowledge Graph Embedding via Nonlinear Dynamic Mapping

      • Zhihao Wang, Honggang Xu, Xin Li, Yuxin Deng
      Pages 141-154

    14. Multi-Grained Dependency Graph Neural Network for Chinese Open Information Extraction

      • Zhiheng Lyu, Kaijie Shi, Xin Li, Lei Hou, Juanzi Li, Binheng Song
      Pages 155-167

    15. Human-Understandable Decision Making for Visual Recognition

      • Xiaowei Zhou, Jie Yin, Ivor Tsang, Chen Wang
      Pages 168-180

    16. LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding

      • Zhiyuan Ning, Ziyue Qiao, Hao Dong, Yi Du, Yuanchun Zhou
      Pages 181-193

    17. Transferring Domain Knowledge with an Adviser in Continuous Tasks

      • Rukshan Wijesinghe, Kasun Vithanage, Dumindu Tissera, Alex Xavier, Subha Fernando, Jayathu Samarawickrama
      Pages 194-205

    18. Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach

      • Weipeng Huang, Nishma Laitonjam, Guangyuan Piao, Neil J. Hurley
      Pages 206-218

    19. Quality Control for Hierarchical Classification with Incomplete Annotations

      • Masafumi Enomoto, Kunihiro Takeoka, Yuyang Dong, Masafumi Oyamada, Takeshi Okadome
      Pages 219-230
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Other volumes

  1. Trends and Applications in Knowledge Discovery and Data Mining

  2. Advances in Knowledge Discovery and Data Mining

  3. Advances in Knowledge Discovery and Data Mining

  4. Advances in Knowledge Discovery and Data Mining

Keywords

About this book

The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021.

The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows:

Part I: Applications of knowledge discovery and data mining of specialized data;

Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics;

Part III: Representation learning and embedding, and learning from data.

Editors and Affiliations

  • IIIT, Hyderabad, Hyderabad, India

    Kamal Karlapalem

  • Chinese University of Hong Kong, Shatin, Hong Kong

    Hong Cheng

  • Virginia Tech, Arlington, USA

    Naren Ramakrishnan

  • Jawaharlal Nehru University, New Delhi, India

    R. K. Agrawal

  • IIIT Hyderabad, Hyderabad, India

    P. Krishna Reddy

  • University of Minnesota, Minneapolis, USA

    Jaideep Srivastava

  • IIIT Delhi, New Delhi, India

    Tanmoy Chakraborty

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