Graph Representation Learning and its Applications (GRLA 2023)


Welcome to GRLA 2023, the 1st workshop on Graph Representation Learning and its Applications. The workshop focus 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.


Topics of interest include but not limited to:

1.Unsupervised node representation learning 
2.Learning representations of entire graphs 
3.Graph neural networks 
4.Graph generation 
5.Heterogeneous graph embedding 
6.Knowledge graph embedding 
7.Graph alignment 
8.Dynamic graph representation 
9.Graph representation learning for relational reasoning 
10.Graph anomaly detection 
11.Applications in recommender systems 
12.Applications in information network analysis 
13.Applications in natural language understanding  
14.Applications in social network analysis 


All submissions should be written in English and submitted via our submission system: paper submitted to GRLA 2023 cannot be under review for any other conference or journal during the entire period that it is considered for DSC 2023, 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 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.


Full paper due: June 25, 2023
Acceptance notification: July 25, 2023
Camera-ready copy: August 2, 2023
Conference Date: August 18-20, 2023



Zhaoyun Ding National University of Defense Technology, Changsha, China

Workshop Co-Chair

Qianzhen Zhang National University of Defense Technology, Changsha, China

Program Committee

Lailong Luo National University of Defense Technology, Changsha, China
Yun Zhou National University of Defense Technology, Changsha, China
Xianqiang Zhu National University of Defense Technology, Changsha, China
Rongfei Zeng Northeastern University, Shenyang, China
Fangda Guo Institute of Computing Technology, Chinese Academy of Sciences, China
Fan Wu Central South University, Changsha, China
Kele Xu National University of Defense Technology, Changsha, China