Shirui Pan

Shirui Pan

Professor and ARC Future Fellow

School of ICT

Griffith University

Biography

Shirui Pan is a Professor and an ARC Future Fellow with the School of Information and Communication Technology, Griffith University, Australia. Before joining Griffith in 2022, he was with the Faculty of Information Technology, Monash University. He received his Ph.D degree in computer science from University of Technology Sydney (UTS), Australia. He is a Senior Member of IEEE and ACM, and a Fellow of Queensland Academy of Arts and Sciences (FQA).

Shirui’s research mainly focuses on artificial intelligence (AI), data mining, and machine learning. He has made contributions to advance graph machine learning methods for solving hard AI problems for real-life applications, including graph classification, anomaly detection, recommender systems, and multivariate time series forecasting. His research has been published in top conferences and journals including NeurIPS, ICML, KDD, TPAMI, TNNLS, and TKDE. He is recognised as one of the AI 2000 AAAI/IJCAI Most Influential Scholars in Australia (2023, 2022), and one of the World’s Top 2% Scientists (2022, 2021). His research received the IEEE ICDM Best Student Paper Award (2020), and the JCDL Best Paper Honorable Mention Award (2020). He has eight papers recognised as the Most Influential Papers in KDD (x1), IJCAI (x5), AAAI (x1), and CIKM (x1) (Feb 2022). He received a prestigious Future Fellowship (2022-2025), one of the most competitive grants from the Australian Research Council (ARC).

潘世瑞（中文简介）

博士招生信息（2023年1月更新）

PhD positions are open! I am looking for self-motivated Ph.D students. See more information here.

Interests
  • Artificial Intelligence
  • Data Mining
  • Machine Learning
  • Deep Learning
  • NLP
  • Graph Neural Networks
  • Trustworthy AI
Education

  • PhD in Computer Science

    University of Technology Sydney

What’s New

Featured Publications

March, 2021 IEEE Transactions on Neural Networks and Learning Systems
A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review on knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference and logical rule reasoning are reviewed. We further explore several emerging topics including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.
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May, 2020 ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-20
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it’s fair to say that existing methods fail to fully exploit latent spatial dependencies between pairs of variables. In recent years, meanwhile, graph neural networks (GNNs) have shown high capability in handling relational dependencies. GNNs require well-defined graph structures for information propagation which means they cannot be applied directly for multivariate time series where the dependencies are not known in advance. In this paper, we propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module, into which external knowledge like variable attributes can be easily integrated. A novel mix-hop propagation layer and a dilated inception layer are further proposed to capture the spatial and temporal dependencies within the time series. The graph learning, graph convolution, and temporal convolution modules are jointly learned in an end-to-end framework. Experimental results show that our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information..
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Recent Publications

IEEE TPAMI, TNNLS, TKDE, TCYB; ICML, NeurIPS, KDD, WWW, CVPR, WSDM, ICDM, AAAI, IJCAI

Yixin Liu, Kaize Ding, Jianling Wang, Vincent Lee, Huan Liu, Shirui Pan (2023). Learning Strong Graph Neural Networks with Weak Information. 29th SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-23, 6 Aug 2023 – 10 Aug 2023, Long Beach, California, United States (CORE A*).

Qin Zhang, Ze Lin Shi, Xiaolin Zhang, Xiaojun Chen, Philippe Fournier-Viger, Shirui Pan (2023). G2Pxy: Generative Open-Set node Classification on Graphs with Proxy Unknowns. The 32nd International Joint Conference on Artificial Intelligence (IJCAI-23), Macao, S.A.R, China, 19th-25th August, 2023 (CORE A*).

He Zhang, Bang Wu, Shuo Wang, Xiangwen Yang, Minhui Xue, Shirui Pan, Xingliang Yuan (2023). Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks. 2023 International Conference on Machine Learning (ICML), Honolulu, Hawaii, USA, July 23 - July 29, 2023 (CORE A*).

Yizhen Zheng, He Zhang, Vincent Lee, Yu Zheng, Xiao Wang, Shirui Pan (2023). Finding the Missing-half: Graph Complementary Learning. 2023 International Conference on Machine Learning (ICML), Honolulu, Hawaii, USA, July 23 - July 29, 2023 (CORE A*).

Linhao Luo, Reza Haffari, Yuan-Fang Li, Shirui Pan (2023). Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion. The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR-23, 23-27 July, 2023, Taipei, Taiwan. (CORE A*).

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Bo Xiong, Mojtaba Nayyeri, Shirui Pan and Steffen Staab (2023). Shrinking Embeddings for Hyper-Relational Knowledge Graphs. The 61st Annual Meeting of the Association for Computational Linguistics (ACL-23), Toronto, Canada, July 9-14, 2023 (CORE A*).

Xin Zheng, Miao Zhang, Chunyang Chen, Qin Zhang, Chuan Zhou, Shirui Pan (2023). Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs. The ACM Web Conference 2023, WWW-23, Austin, Texas, USA, April 30 - May 4, 2023 (CORE A*).

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Yang Liu, Chuan Zhou, Shirui Pan, Jia Wu, Zhao Li, Hongyang Chen, Peng Zhang (2023). CurvDrop: A Ricci Curvature Based Approach to Prevent Graph Neural Networks from Over-Smoothing and Over-Squashing. The ACM Web Conference 2023, WWW-23, Austin, Texas, USA, April 30 - May 4, 2023 (CORE A*).

Code

Di Jin, Luzhi Wang, Yizhen Zheng, Guojie Song, Fei Jiang, Xiang Li, Wei Lin, Shirui Pan (2023). Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation. The ACM Web Conference 2023, WWW-23, Austin, Texas, USA, April 30 - May 4, 2023 (CORE A*).

Bingxin Zhou, Yuanhong Jiang, Yuguang Wang, Jingwei Liang, Junbin Gao, Shirui Pan, Xiaoqun Zhang (2023). Robust Graph Representation Learning for Local Corruption Recovery. The ACM Web Conference 2023, WWW-23, Austin, Texas, USA, April 30 - May 4, 2023 (CORE A*).

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Team Leader

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Shirui Pan

Professor and ARC Future Fellow

PhD Students

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Yixin Liu

PhD Student @ Monash (01/2021-)

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Bang Wu

PhD Student @ Monash (08/2020-)

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He Zhang

PhD Student @ Monash (04/2021-)

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Linhao Luo

PhD Student @ Monash (02/2022-)

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Yizhen Zheng

PhD Student @ Monash (07/2021-)

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Bo Xiong

PhD Student @ Universität Stuttgart (09/2020-)

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Luzhi Wang

PhD Student @ Tianjin U. (07/2020-)

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Xin Zheng

PhD Students @ Monash (04/2021-)

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Yuanzhe Zhang

PhD Student @ Monash (06/2021-)

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Ming Jin

PhD Student @ Monash (08/2020-)

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Huan Yee Koh

PhD Student @ Monash (04/2022-)

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Jiaxin Ju

PhD Student @ Griffith University (02/2023-)

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Dongran Yu

PhD Student @ Jili (09/2019-)

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Dongwon Ryu

PhD Student @ Monash (02/2021-)

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Guangsi Shi

PhD Student @ Monash (07/2020-)

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Mohit Gupta

PhD Student @ Monash (01/2022-)

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Zahra Zamanzadeh

PhD Student @ Monash (01/2022)

Alumni

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Miao Zhang

Professor @ Harbin Institute of Technology

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Shichao Zhu

AI Scientist @ ByteDance

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Zonghan Wu

CEO

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Man Wu

PhD @ FAU (2020-2022)

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Hong Yang

Associate Professor @ Guangzhou University

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Sheng Wan

PostDoc @ NUST

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Chun Wang

Assistant Professor @ City University of Macau

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Ruiqi Hu

CEO

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Shaoxiong Ji

Postdoc @ University of Helsinki

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Guojia Wan

Postdoc @ Wuhan U.

Awards and Grants

Shirui Pan’s research was supported by Australian Research Council (ARC), Defence Science and Technology Group (DSTG), Amazon, Metso Outotec, Shanghai Aircraft Manufacturing Co, Ltd, etc.

  • [Award]: Fellow of Queensland Academy of Arts and Sciences (QAAS).

  • [Award]: Amazon Research Awards (APA) Fall 2021 round.

  • [Award]: AI 2000 AAAI/IJCAI Most Influential Scholars Honorable Mention (2022) (only three recipients in Australia) (25/01/2021).

  • [Award]: Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge ($800,000 from ARC and $470,000 from Monash University) - 2021-2025

    • ARC Future Fellow
    • Australian Research Council (ARC)

  • [Award]: 5 Papers are Selected as Most Influential Papers in IJCAI (02/2022).

    • 2021 Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning, Ming Jin et al (Rank: 12)
    • 2019 Graph WaveNet For Deep Spatial-Temporal Graph Modeling, Zonghan Wu et al (Rank: 1)
    • 2019 Attributed Graph Clustering: A Deep Attentional Embedding Approach, Chun Wang et al (Rank: 9)
    • 2018 Adversarially Regularized Graph Autoencoder For Graph Embedding, Shirui Pan et al (Rank: 4)
    • 2016 Tri-Party Deep Network Representation, IJCAI-2016, Shirui Pan et al (Rank: 6)

  • [Award]: 1 Paper is Selected as Most Influential Paper in KDD (02/2022).

    • 2020 Connecting The Dots: Multivariate Time Series Forecasting With Graph Neural Networks, Zonghan Wu et al (Rank: 4)

  • [Award]: 1 Paper is Selected as Most Influential Paper in AAAI (02/2022).

    • 2018 DiSAN: Directional Self-Attention Network For RNN/CNN-Free Language Understanding, Tao Shen et al (Rank: 11)

  • [Award]: 1 Paper is Selected as Most Influential Paper in CIKM (02/2022).

    • 2017 MGAE: Marginalized Graph Autoencoder For Graph Clustering, CIKM-2017, Chun Wang et al (Rank: 9)

  • [Award]: 2021 FIT Dean’s Award for Excellence in Research by an Early Career Researcher

  • [Award]: AI 2000 AAAI/IJCAI Most Influential Scholars Honorable Mention (2021) (only five recipients in Australia) (08/04/2021).

  • Best Student Paper Award for ICDM-2020 (CORE A* conference)

  • The Vannevar Bush Best Paper Honorable Mention for JCDL-2020 (CORE-2018 A* conference)

  • Anomaly Detection in Social Networks ($11,000) - 2019-2020

    • Faculty Early Career Researcher Grant
    • Monash University

  • Cyberbullying Detection on Social Networks ($20,000) - 2016-2017

    • UTS Early Career Researcher Grant (UTS - ECRG)
    • University of Technology Sydney

Professional Services

Editorship

  • Associate Editor: IEEE Transactions on Cybernetics (TCYB) (IF: 19.11)(2023-)
  • Associate Editor: Neural Networks (IF: 9.657)(2023-)
  • Associate Editor: Knowledge and Information Systems (IF: 2.531) (2022-)
  • Guest Editor for Special Issue: Pattern Recognition (2022)
  • Guest Editor for Special Issue: IEEE Transactions on Neural Networks and Learning Systems (2021)
  • Guest Editor for Special Issue: Future Generation Computer Systems (2020)
  • Guest Editor for Special Issue: Neurocomputing (2020)
  • Guest Editor for Special Issue: Complexity (2019)
  • Associate Editor: IEEE Access (2018-2019)

Conference Organisation

  • Proceeding Co-chair: 2022 IEEE International Conference on Data Mining(ADMA-2022)
  • Tutorial Chair: 2022 IEEE International Conference on Data Mining(ICDM-2022)
  • Program Chair: 2021 International Conference on Data Science and Systems (DSS-2021)
  • Workshop Chair: 2021 IEEE International Conference on Big Data (IEEE BigData-2021)
  • Tutorial Chair: 2020 Digital Image Computing: Techniques and Applications (DICTA-2020)
  • Special Session Chair: Learning from Big Graph Data: Theory and Applications, IJCNN-2018 (CORE A)
  • Special Session Chair: Advanced Data Analytics for Large-scale Complex Data Environment, IJCNN- 2017 (CORE A)
  • Special Session Chair: Advanced Machine Learning Methods and Applications from Complicated Data Environment, IJCNN-2016 (CORE A)

Journal Reviewer

  • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (CORE A*)
  • IEEE Transactions on Neural Networks and Learning Systems (TNNLS) (CORE A*)
  • IEEE Transactions on Knowledge and Data Engineering (TKDE)((CORE A*)
  • IEEE Transactions on Cybernetics (TCYB) (CORE A)
  • IEEE Transactions on Systems, Man and Cybernetics: Systems (SMCA) (CORE B)
  • IEEE Signal Processing Letters
  • ACM Transactions on Intelligent Systems and Technology (TIST)
  • World Wide Web (WWW) (CORE A)
  • Social Network Analysis and Mining
  • Neural Computing and Applications
  • Neurocomputing
  • Information Sciences (CORE A)

(Senior) Program Committee

  • The International Conference on Learning Representations (ICLR 23-21)
  • Annual Conference on Neural Information Processing Systems (NeurIPS 22-19) (CORE A*)
  • SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 22-19) (CORE A*)
  • Conference on Computer Vision and Pattern Recognition (CVPR 22-21) (CORE A*)
  • IEEE International Conference on Data Mining (ICDM 22-19) (CORE A*)
  • AAAI Conference on Artificial Intelligence (AAAI 23-17) (CORE A*)
  • The Web Conference (WWW 23-19) (CORE A*)
  • International Joint Conference on Artificial Intelligence (IJCAI 22-17) (CORE A*)
  • SIAM International Conference on Data Mining (SDM-19) (CORE A)
  • Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-19,18,17,16)(CORE A)
  • IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2014-15)
  • International Conference on Advanced Data Mining and Applications (ADMA 2014, 2016)
  • The international conference on Advances in Social Network Analysis and Mining (ASONAM 2017)
  • The China Computer FedCOREtion (CCF) Conference on Big Data (CCF Big Data 2017)

Recent & Upcoming Talks

Source Codes

Curated List

A list of open source code is maintained on Github. Other source code will be released as it is ready for publishing.

Graph Classification

Teaching

  • FIT5196: Data Wrangling, Lecturer, 2019 S1, Monash University
  • FIT5196: Data Wrangling, Lecturer, 2020 S2, Monash University
  • FIT5196: Data Wrangling, Chief Examiner & Lecturer, 2021 S2, Monash University
  • FIT5212: Data Analysis for Semi-Structured Data, Co-Chief Examiner & Lecturer, 2020 S1, Monash University
  • FIT5212: Data Analysis for Semi-Structured Data, Co-Chief Examiner & Lecturer, 2021 S1, Monash University

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