Publications

📚 Publications & Research Output

Welcome to my research portfolio!

🌟 Selected Publications

Highlighting key contributions across my research areas

📖 Knowledge-augmented Machine Learning

1. Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey
   Zhiqiang Zhong, Anastasia Barkova, Davide Mottin
   ACM Computing Surveys (CSUR) IF 23.8 🏆
   [Paper] [GitHub Project Page]

🧠 Graph Machine Learning

1. Hierarchical Message-Passing Graph Neural Networks
   Zhiqiang Zhong, Cheng-Te Li, Jun Pang
   Data Mining and Knowledge Discovery (DMKD) - Journal Track of ECML-PKDD
   [Paper] [Code]

🤖 Large Language Models

1. Harnessing Large Language Models as Post-hoc Correctors
   Zhiqiang Zhong, Kuangyu Zhou, Davide Mottin
   ACL 2024
   [Paper] [Code]

🧬 AI for Science

1. Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language
   Zhiqiang Zhong, Simon Sataa-Yu Larsen, Haoyu Guo, Tao Tang, Kuangyu Zhou, Davide Mottin
   NAACL 2025
   [Paper] [Code]

📅 Complete Publication List

2025

1. “Double vaccinated, 5G boosted!”: Learning Attitudes towards COVID-19 Vaccination from Social Media
   Ninghan Chen, Xihui Chen, Zhiqiang Zhong, Jun Pang
   ACM Transactions on the Web
   [Paper]

2. Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language
   Zhiqiang Zhong, Simon Sataa-Yu Larsen, Haoyu Guo, Tao Tang, Kuangyu Zhou, Davide Mottin
   NAACL 2025
   [Paper] [Code]

3. 📄 EVOL3D: A Large-Scale 3D Structure Dataset for Investigating Mutational Effects in Homologous Proteins
   Zhiqiang Zhong, Yinghua Yao, Davide Mottin, Jun Pang
   AI4X 2025 International Conference
   [Paper] [Dataset Page]

4. Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey
   Zhiqiang Zhong, Anastasia Barkova, Davide Mottin
   ACM Computing Surveys (CSUR) IF 23.8 🏆
   [Paper] [GitHub Project Page]

2024

1. Harnessing Large Language Models as Post-hoc Correctors
   Zhiqiang Zhong, Kuangyu Zhou, Davide Mottin
   ACL 2024
   [Paper] [Code]

2. Exploring Graph Structure Comprehension Ability of Multimodal Large Language Models: Case Studies
   Zhiqiang Zhong, Davide Mottin
   Learning on Graphs Conference (LoG’24)
   [Paper] [Code]

3. Efficiently Predicting Mutational Effect on Homologous Proteins by Evolution Encoding
   Zhiqiang Zhong, Davide Mottin
   ECML-PKDD 2024
   [Paper] [Code]

4. Hierarchical Bipartite Graph Convolutional Network for Recommendation
   Yi Wei Cheng, Zhiqiang Zhong, Jun Pang, Cheng-Te Li
   IEEE Computational Intelligence Magazine (CIM)
   [Paper] [Code]

5. Bridging Performance of X (formerly known as Twitter) Users: A Predictor of Subjective Well-Being During the Pandemic
   Ninghan Chen, Xihui Chen, Zhiqiang Zhong, Jun Pang
   ACM Transactions on the Web
   [Paper] [Code]

6. A tale of two roles: exploring topic-specific susceptibility and influence in cascade prediction
   Ninghan Chen, Xihui Chen, Zhiqiang Zhong, Jun Pang
   Data Mining and Knowledge Discovery (DMKD) - Journal Track of ECML-PKDD
   [Paper]

7. Benchmarking Large Language Models for Molecule Prediction Tasks
   Zhiqiang Zhong, Kuangyu Zhou, Davide Mottin
   arXiv Preprint
   [Paper] [Code]

2023

1. On the Robustness of Post-hoc GNN Explainers to Label Noise
   Zhiqiang Zhong, Yangqianzi Jiang, Davide Mottin
   Learning on Graphs Conference (LoG’23)
   [Paper] [Code]

2. How Faithful are Self-Explainable GNNs?
   Marc Christiansen, Lea Villadsen, Zhiqiang Zhong, Stefano Teso, Davide Mottin
   Learning on Graphs Conference (LoG’23)
   [Paper] [Code]

3. Knowledge-augmented Graph Machine Learning for Drug Discovery: From Precision to Interpretability
   Zhiqiang Zhong, Davide Mottin
   KDD 2023
   [Paper] [Web Page]

4. Multi-grained Semantics-aware Graph Neural Networks
   Zhiqiang Zhong, Cheng-Te Li, Jun Pang
   IEEE Transactions on Knowledge and Data Engineering (TKDE)
   [Paper] [Code]

2022

1. Hierarchical Message-Passing Graph Neural Networks
   Zhiqiang Zhong, Cheng-Te Li, Jun Pang
   Data Mining and Knowledge Discovery (DMKD) - Journal Track of ECML-PKDD
   [Paper] [Code]

2. Unsupervised Network Embedding Beyond Homophily
   Zhiqiang Zhong, Guadalupe Gonzalez, Daniele Grattarola, Jun Pang
   Transactions on Machine Learning Research (TMLR)
   [Paper] [Code]

3. Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation
   Zhiqiang Zhong, Sergey Ivanov, Jun Pang
   Transactions on Machine Learning Research (TMLR)
   [Paper] [Code]

4. Personalised Meta-path Generation for Heterogeneous Graph Neural Networks
   Zhiqiang Zhong, Cheng-Te Li, Jun Pang
   Data Mining and Knowledge Discovery (DMKD) - Journal Track of ECML-PKDD
   [Paper] [Code]

5. Exploring Spillover Effects for COVID-19 Cascade Prediction
   Ninghan Chen, Xihui Chen, Zhiqiang Zhong, Jun Pang
   Journal of Entropy
   [Paper]

6. The Burden of Being a Bridge: Analysing Subjective Well-Being of Twitter Users during the COVID-19 Pandemic
   Ninghan Chen, Xihui Chen, Zhiqiang Zhong, Jun Pang
   ECML-PKDD 2022
   [Paper]

2021

1. From #Jobsearch to #Mask: Improving COVID-19 Cascade Prediction with Spillover Effects
   Ninghan Chen, Xihui Chen, Zhiqiang Zhong, Jun Pang
   ASONAM 2021
   [Paper]

2. An Exploratory Study of COVID-19 Information on Twitter in the Greater Region
   Ninghan Chen, Zhiqiang Zhong, Jun Pang
   Journal of Big Data and Cognitive Computing
   [Paper]

2020

1. NeuLP: An End-to-end Deep-learning Model for Link Prediction
   Zhiqiang Zhong, Yang Zhang, Jun Pang
   WISE 2020
   [Paper] [Code]

2019

1. A Graph-based Approach to Explore Relationship between Hashtags and Images
   Zhiqiang Zhong, Yang Zhang, Jun Pang
   WISE 2019
   [Paper]

Last updated: September 2025 ✨