I am currently an Assistant Researcher at the School of Artificial Intelligence, Tianjin University. My research focuses on Graph Foundation Models, Multimodal Graph Learning, and AI4Science. I have published over 30 papers and delivered tutorials on network role analysis at ICDM-2021 and DSAA-2021. I serve as a reviewer for leading conferences (e.g., AAAI, KDD, IJCAI, CIKM) and journals (e.g., TNNLS, TCYB, KBS). Prospective students are welcome to contact me for discussion and collaboration.
我是天津大学人工智能学院助理研究员郭翾，主要研究领域为图基础模型、多模态图学习、AI4Science等。 发表CCF推荐会议、SCI期刊论文30余篇。在数据挖掘会议ICDM-2021和DSAA-2021上开展网络角色分析相关教程。 担任人工智能、数据挖掘等领域 AAAI、KDD、IJCAI、CIKM等会议和TNNLS、TCYB、KBS等期刊的审稿人。 主持国家重大科技专项子课题1项，合成生物技术全国重点实验室自主创新基金1项，中兴产学研项目1项。 欢迎有意向的学生联系我交流探讨。
Conference Journal publications are highlighted. Presentation slides and extra materials are sometimes included.
⭐ Main Publications
- Learning Heterogeneous Network Representations for Relation Prediction via Characterizing Hierarchical and Anisotropic Generation Process
- 自适应建模网络动力学的动态链路预测方法 (Dynamic Link Prediction Method for Adaptively Modeling Network Dynamics)
- Counterfactual Learning for Higher-Order Relation Prediction in Heterogeneous Information Networks
- Learning Node Representations via Sketching the Generative Process with Events Benefits Link Prediction on Heterogeneous Networks
- Representation Learning on Heterostructures via Heterogeneous Anonymous Walks
- Structure-Enhanced Graph Neural ODE Network for Temporal Link Prediction
- Learning Stochastic Equivalence based on Discrete Ricci Curvature
- Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism
- A Survey on Role-Oriented Network Embedding
- Role-Oriented Graph Auto-Encoder Guided by Structural Information
📚 Other Publications
- Unified Network Embedding via Mutual Fusion of Communities and Roles
- HSDP: Hypergraph and structure-aware representation learning for information diffusion prediction
- Towards Robust Heterogeneous Graph Explanations under Structural Perturbations
- Towards OOD Generalization in Dynamic Graphs via Causal Invariant Learning
- TGFormer: Towards Temporal Graph Transformer with Auto-Correlation Mechanism
- HGMP: Heterogeneous Graph Multi-Task Prompt Learning
- A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities
- Graph Contrastive Learning with Node-Level Accurate Difference
- Learning Accurate Neighborhood-and Self-Information for Higher-Order Relation Prediction in Heterogeneous Information Networks
- 图神经常微分方程综述 (Survey on Graph Neural Ordinary Differential Equations)
- Disentangled Representation Learning for Structural Role Discovery
- HGN2T: A Simple but Plug-and-Play Framework Extending HGNNs on Heterogeneous Temporal Graphs
- Graph Contrastive Learning via Interventional View Generation
- Network Alignment Enhanced via Modeling Heterogeneity of Anchor Nodes
- Role Discovery-Guided Network Embedding Based on Autoencoder and Attention Mechanism
- Role-Oriented Network Embedding Based on Adversarial Learning between Higher-order and Local Features
- Role-Based Network Embedding via Structural Features Reconstruction with Degree-Regularized Constraint
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Roles in Networks - Foundations, Methods and Applications
- May 2026🎉 Paper accepted to ESWA: "Learning Heterogeneous Network Representations for Relation Prediction via Characterizing Hierarchical and Anisotropic Generation Process"论文被 ESWA 接收：Learning Heterogeneous Network Representations for Relation Prediction via Characterizing Hierarchical and Anisotropic Generation Process
- May 2026🎉 Congratulations to Liu Shilong for his paper "AgentsKG: A Hierarchical Multi-Agent Framework for Open-Domain Knowledge Graph Construction" accepted to KDD 2026!🎉 恭喜刘世龙的论文“AgentsKG: A Hierarchical Multi-Agent Framework for Open-Domain Knowledge Graph Construction”被 KDD 2026 接收！