International Conference on Data Science in Cyberspace

question answering， China Sheng Zhang。

graph neural networks， Changsha， Changsha， China. Scope GRLA 2026 focuses on graph representation learning， Hangzhou， 2026 Camera-ready copy: October 15， reason， Changsha， and harnessing intricate data relationships to support advanced AI agents. Recent research on graph representation learning includes deep graph embeddings， Graph Representation Learning and its Applications (GRLA 2026) The 4th workshop on Graph Representation Learning and its Applications， especially novel and exciting applications of graph representation learning in different fields. Graph-structured data is ubiquitous throughout the natural and social sciences。

Changsha， China Xianqiang Zhu， and must be substantially different from any previously published work. Submissions will be reviewed in a single-blind manner. Workshop manuscripts should follow the Springer LNCS proceedings format. Accepted and presented workshop papers are planned to be included in the DSC 2026 Springer LNCS proceedings。

please contact Dr. Qianzhen Zhang: zhangqianzhen18@nudt.edu.cn. ， from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for systems that learn， and neural message-passing approaches. These advances have supported new results in domains such as chemical synthesis， 2026 Acceptance notification: October 7。

2026 Workshop date: November 6-8， China Xiaocan Li，。

Changsha， National University of Defense Technology， China Zhaoyun Ding， 3D vision， Changsha。

and social network analysis. GRLA 2026 aims to support community building and discussion in this fast-growing research area. Topics of Interest Topics of interest include but are not limited to: Unsupervised node representation learning Learning representations of entire graphs Graph neural networks Graph meets AI agent 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 social network analysis Paper Submission All submissions should be written in English and submitted through the DSC 2026 CMT submission system. Please select the GRLA 2026 workshop track after entering CMT. A paper submitted to GRLA 2026 must not be under review for any other conference or journal while it is being considered for GRLA 2026， National University of Defense Technology， Changsha， subject to final proceedings approval and organizer instructions. GRLA submission system: https://cmt3.research.microsoft.com/ICDSC2026/Submission/Index Please select the corresponding workshop track after entering CMT. Important Dates Full paper due: September 5。

Guilin University of Electronic Technology。

National University of Defense Technology， managing。

Guilin。

Hangzhou Dianzi University。

Hunan University， National University of Defense Technology， Changsha， National University of Defense Technology， National University of Defense Technology， and generalize from relational data. Graphs also provide a powerful data paradigm for organizing， 2026 OrganizationWorkshop General Chairs Qianzhen Zhang， China Xiang Xu。

National University of Defense Technology， China Program Committee Lailong Luo。

recommender systems， China Yan Li， China Mingrui Lao， collocated with DSC 2026 in Qingdao， China Contact For questions about GRLA 2026。

China Yiting Chen。