My primary research areas include large models, graph machine learning, network security, blockchain, computer vision, and human-computer (robot) interaction. More broadly, my work encompasses machine learning, deep learning, data mining, and their applications in intelligent systems, with a particular focus on developing efficient and scalable models to handle complex structured data and optimizing their applications in security, distributed computing, and intelligent interaction.
Specifically, I have conducted in-depth research on heterogeneous graph learning, probabilistic graphical models, and federated learning, proposing a series of modeling methods for heterogeneous graph data, optimizing cross-domain information fusion and knowledge distillation strategies, and exploring optimization mechanisms for heterogeneous graphs in federated environments. Additionally, I have investigated graph learning-based blockchain security analysis and task allocation optimization methods, constructing transaction graphs to accurately detect abnormal transactions and integrating graph attention mechanisms to enhance the intelligence of task scheduling. Meanwhile, I focus on multimodal information fusion in computer vision and human-computer interaction, exploring how to leverage multi-source data such as vision and speech to enhance intelligent systems’ understanding and decision-making capabilities.
My recent research focuses on integrating multimodal data to optimize the applications of graph machine learning and large models in network security, computer vision, and blockchain, including efficient heterogeneous graph federated learning, cross-modal emotion recognition, and intelligent task scheduling optimization to address increasingly complex real-world scenarios.

Projects Led and Participated In

1. Shandong Provincial Key R&D Program: Construction and Application of a Marine Ecological Big Data Cloud Storage Platform for Shandong’s Coastal Waters (Completed), Principal Investigator.
2. Shandong Provincial Natural Science Youth Fund Project: Research on CP-nets and Their Aggregation (Completed), Principal Investigator.
3. Shandong Provincial Key R&D Program: Visual Simulation and Computational Evolutionary Game Theory (Ongoing), Technical Lead.
4. Yantai City University-Local Integration Project: Digital Twin Service Platform for Marine Environment and Unmanned Equipment (Ongoing), Technical Lead.
5. Yantai Major Innovation Project: Multimodal Data and Mobile Application AI Security Monitoring Platform for Cyberspace (Ongoing), Project Lead.
6. Yantai Science and Technology Development Plan: Research and Development of Key Technologies for Human-Machine Collaboration in Industrial Internet Scenarios Driven by Intelligent Vision (Ongoing), Principal Investigator.

Representative Papers

1. Yixian Wang, Zhaowei Liu, Jindong Xu, Weiqing Yan. Heterogeneous Network Representation Learning Approach for Ethereum Identity Identification [J]. *IEEE Transactions on Computational Social Systems, 2023, 10(3): 890-899. (Nominated for the Andrew P. Sage Best Transactions Paper Award)
2. Zhaowei Liu, Dong Yang, Yingjie Wang, Mingjie Lu, Ranran Li. EGNN: Graph Structure Learning based on Evolutionary Computation Helps More in Graph Neural Networks [J]. Applied Soft Computing, 2023, 135: 110040.
3. Zhaowei Liu, Yixian Wang, Shenqiang Wang. Heterogeneous Graphs Neural Networks based on Neighbor Relationship Filtering [J]. Expert Systems with Applications, 2024, 239: 122489.
4. Zhaowei Liu, Dong Yang, Shenqiang Wang, Hang Su. Adaptive Multi-channel Bayesian Graph Attention Network for IoT Transaction Security [J]. Digital Communications and Networks, 2022.
5. Zhaowei Liu, Zongxing Zhao. Multi-attribute E-CARGO Task Assignment Model Based on Adaptive Heterogeneous Residual Networks [J]. IEEE Transactions on Computational Social Systems, 2024.
6. Rufei Gao, Zhaowei Liu, Chenxi Jiang, Yingjie Wang, Shenqiang Wang, Pengda Wang. BI-FedGNN: Federated Graph Neural Networks Framework based on Bayesian Inference [J]. *Neural Networks, 2024, 169: 143-153.
7. Dezhi Guo, Zhaowei Liu, Ranran Li. RegraphGAN: A Graph Generative Adversarial Network Model for Dynamic Network Anomaly Detection [J]. *Neural Networks, 2023, 166: 273-285.
8. Zongxing Zhao, Zhaowei Liu, Yingjie Wang, Dong Yang, Weishuai Che. RA-HGNN: Attribute Completion of Heterogeneous Graph Neural Networks based on Residual Attention Mechanism [J]. *Expert Systems with Applications, 2024, 243: 122945.
9. Pengda Wang, Mingjie Lu, Weiqing Yan, Dong Yang, Zhaowei Liu. Graph Structure Learning With Automatic Search of Hyperparameters Based on Genetic Programming [J]. *IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, DOI: 10.1109/TETCI.2024.3386833.
10. Dong Yang, Zhaowei Liu, Yingjie Wang, Jindong Xu, Weiqing Yan, Ranran Li. Adaptive Multi-channel Bayesian Graph Neural Network [J]. *Neurocomputing, 2024, 575: 127260.

Journal reviewer

1. IEEE Transactions on Mobile Computing (TMC)
2. IEEE Transactions on Cloud Computing (TCC)
3. IEEE Transactions on Network Science and Engineering (TNSE)
4. IEEE Transactions on Automation Science and Engineering (TASE)
5. IEEE Transactions on Cognitive and Developmental Systems (TCDS)
6. IEEE Transactions on Computational Social Systems (TCSS)
7. Information Processing & Management (IP&M)
8. Expert Systems with Applications (ESWA)
9. Applied Soft Computing (ASOC)
10. Neural Networks (NN)

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