Scope
Welcome to GRLA 2025, the 3rd workshop on Graph Representation Learning and its Applications. The workshop focuses on graph representation learning, especially novel and exciting applications of graph representation learning in different fields.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial if we want systems that can learn, reason, and generalize from this kind of data. Furthermore, graphs can be seen as a natural generalization of simpler kinds of structured data (such as images), and therefore, they represent a natural avenue for the next breakthroughs in machine learning.
Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph neural networks and related techniques have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D-vision, recommender systems, question answering, and social network analysis.
The primary goal for this workshop is to facilitate community building; with hundreds of new researchers beginning projects in this area, we hope to bring them together to consolidate this fast-growing area of graph representation learning into a healthy and vibrant subfield.
WORKSHOP AREAS
Topic interest include but not limited to:
- Unsupervised node representation learning
- Learning representations of entire graphs
- Graph neural networks
- Graph generation
- Heterogeneous graph embedding
- Knowledge graph embedding
- Graph alignment
- Dynamic graph representation
- Graph representation learning for relational reasoning
- Graph anomaly detection
- Applications in recommender systems
- Applications in information network analysis
- Applications in natural language understanding
- Applications in social network analysis
PAPER SUBMISSION
All submissions should be written in English and submitted via our submission system.A paper submitted to GRLA 2025 cannot be under review for any other conference or journal during the entire period that it is considered for DSC 2025, and must be substantially different from any previously published work. Submissions are reviewed in a single-blind manner. Please note that all submissions must strictly adhere to the IEEE templates as provided at http://dsc.pcl.ac.cn/2025/submission.html. The templates also act as a guideline regarding formatting. In particular, all submissions must use either the LATEX template or the MS-Word template. Please follow exactly the instructions below to ensure that your submission can ultimately be included in the proceedings. If you have any question on GRLA 2025, please feel free to contact Dr. Qianzhen Zhang: zhangqianzhen18@nudt.edu.cn
IMPORTANT DATES
ORGANIZATION
Workshop General Chair
Qianzhen Zhang, National University of Defense Technology, Changsha, China
Zhaoyun Ding, National University of Defense Technology, Changsha, China
Program Committee
Lailong Luo, National University of Defense Technology, Changsha, China
Xianqiang Zhu, National University of Defense Technology, Changsha, China
Jianfeng Zhang, National University of Defense Technology, Changsha, China
Xiaocan Li, Hunan University, Changsha, China
Ning Liu, Shandong University, Jinan, China
Mingrui Lao, National University of Defense Technology
Yiting Chen, Guilin University of Electronic Technology, Guilin, China
Xiang Xu, Hangzhou Dianzi University, Hangzhou, China
Meixuan Li, National University of Defense Technology, Changsha, China
Shen Zhang, National University of Defense Technology, Changsha, China
