Aim and Scope:
Graph-based learning with LLMs has emerged as a powerful paradigm for modeling structured data across scientific domains. In particular, its applications in healthcare are gaining increasing attention with the integration of LLM-driven methods, neural-symbolic reasoning, and advanced graph neural networks. This special session focuses on novel graph learning frameworks and LLM-based intelligent systems that bridge symbolic biomedical knowledge and data-driven models to enhance interpretability, robustness, and generalization in complex domains such as medicine recommendation, drug discovery, protein design, and other related human healthcare.
The session welcomes original contributions that explore topics including, but not limited to:
1. LLM-driven agent design for human healthcare.
2. Graph neural networks for molecules, reactions, and materials
3. LLM-powered knowledge graph construction and integration for scientific discovery
4. Neural-symbolic reasoning for chemistry and materials science
5. Graph pretraining, transfer learning, and data-efficient modeling
6. Interpretable and explainable graph models for science applications
7. Hybrid architectures combining physics, knowledge, and learning
Acknowledgements:
This special session is organized by Dr. Tengfei Ma (Hunan University, China).
Introduction to organizers:
Tengfei Ma is a Ph.D. candidate at the College of Computer Science and Electronic Engineering, Hunan University. He obtained his M.S. from Hunan University. His research interests lie in LLMs, structured prediction, graph representation learning, and their applications to healthcare. He has published multiple papers in top-tier conferences and journals such as ICLR, AAAI, IJCAI, CIKM, and TKDE. He is particularly interested in integrating domain knowledge with neural architectures to solve real-world scientific problems.
Submission Process:
If you wish to participate in this special session--KGLDIH, please submit your manuscript through the ConfSync:https://confsync.cn/csae/submission and select the Section "Knowledge Graph and LLM-Driven Intelligence for Healthcare".We will assign your submission to Dr.Tengfei Ma for a preliminary review. After passing the preliminary review, your manuscript will undergo a secondary review by experts. Notifications of acceptance will be issued concurrently with the main conference notifications. For any questions, please contact: info@confsync.cn.