Be Humble. Stay Hungry. Always Hustle.

Kaize Ding(丁凯泽)

Ph.D. Student

Data Mining and Machine Learning Laboratory

School of Computing, Informatics, and Decision Systems Engineering

Arizona State University

Email: kaize.ding AT asu DOT edu


About Me

I am a PhD student of Computing Science and Engineering, Arizona State University. I work as a research assistant at Data Mining and Machine Learning Laboratory, advised by Professor Huan Liu.

Before that, I obtained my master and bachelor degrees from Beijing University of Posts and Telecommunications(BUPT).


Research Interests

My research interests generally lie in data mining and machine learning, recently I'm focusing on graph neural networks, and its applications such as anomaly detection, recommendation, etc.


News

[01/2021] One paper got accepted in WWW 2021.

[12/2020] One paper got accepted in SDM 2021.

[12/2020] One paper got accepted in AAAI 2021.

[12/2020] Invited to be PC member of NAACL 2021, ACL 2021.

[09/2020] One paper got accepted in EMNLP 2020.


Publications

Preprint Papers:

  1. Feature Interaction-aware Graph Neural Networks [pdf] [bib]
    Kaize Ding, Yichuan Li, Jundong Li, Chenghao Liu and Huan Liu

Refereed Papers:

2021:

  1. Few-shot Network Anomaly Detection with Cross-network Meta-learning [pdf] [bib]
    Kaize Ding, Qinghai Zhou, Hanghang Tong and Huan Liu
    In Proceedings of The Web Conference (formerly WWW), 2021

  2. Fact-enhanced Synthetic News Generation [pdf] [bib]
    Kai Shu, Yichuan Li, Kaize Ding and Huan Liu
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2021

  3. Session-based Recommendation with Hypergraph Attention Networks [pdf] [code]
    Jianling Wang, Kaize Ding, Ziwei Zhu and James Caverlee
    In Proceedings of the SIAM International Conference on Data Mining (SDM), 2021

2020:

  1. Be More with Less: Hypergraph Attention Networks for Inductive Text Classification [pdf] [code]
    Kaize Ding, Jianling Wang, Jundong Li, Dingchneg Li, and Huan Liu
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020

  2. Graph Prototypical Networks for Few-shot Learning on Attributed Networks [pdf] [code]
    Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, and Huan Liu
    In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM), 2020

  3. Inductive Anomaly Detection on Attributed Networks [pdf] [bib]
    Kaize Ding, Jundong Li, Nitin Aagarwal and Huan Liu
    In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2020

  4. Key Opinion Leaders in Recommendation Systems: Opinion Elicitation and Diffusion [pdf] [bib]
    Kaize Ding*, Jianling Wang*, Ziwei Zhu, Yin Zhang and James Caverlee (*equal contribution)
    In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2020

  5. Next-item Recommendation with Sequential Hypergraphs [pdf] [bib]
    Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu and James Caverlee
    In Proceedings of the ACM International SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020

  6. Graph Few-shot Learning with Attribute Matching [pdf] [bib]
    Ning Wang, Minnan Luo, Kaize Ding, Lingling Zhang, Jundong Li, and Qinghua Zheng
    In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM), 2020

  7. Combating Disinformation in a Social Media Age [pdf] [bib]
    Kai Shu, Amrita Bhattacharjee, Faisal Alatawi, Tahora H. Nazer, Kaize Ding, Mansooreh Karami, and Huan Liu
    WIREs Data Mining and Knowledge Discovery, 2020

  8. Challenges in Combating COVID-19 Infodemic -- Data, Tools, and Ethics [pdf] [bib]
    Kaize Ding, Kai Shu, Yichuan Li, Amrita Bhattacharjee and Huan Liu
    CIKM 2020 Workshop on Mining Actionable Insights from Social Networks (MAISoN), 2020

2019:

  1. InterSpot: Interactive Spammer Detection in Social Media (demo paper) [pdf] [bib]
    Kaize Ding, Jundong Li, Shivam Dhar, Shreyash Devan, and Huan Liu
    In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2019

  2. Deep Anomaly Detection on Attributed Networks with Graph Convolutional Networks [pdf] [code]
    Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu
    In Proceedings of the SIAM International Conference on Data Mining (SDM), 2019

  3. Interactive Anomaly Detection on Attributed Networks [pdf] [code]
    Kaize Ding, Jundong Li, and Huan Liu
    In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2019


Honors and Awards


Services

Program Committee:

IJCAI'20, ECML-PKDD'20

External Reviewer:

KDD'19, WWW'19, SIGIR'18, ASONAM'18


Experiences