About to GNNCS 2026
GNNCS 2026 will be held in Beijing. As a national center for scientific and technological innovation, Beijing has gathered top universities and research institutions in the fields of artificial intelligence and complex systems research. The conference focuses on innovation in graph neural network architecture, complex system analysis methods, and cross domain applications, covering core directions such as graph convolutional networks, network dynamics, and system emergent behavior. The attending experts will delve into cutting-edge topics such as interpretability of graph neural networks, dynamic graph learning, robustness of complex networks, and multi-agent collaborative evolution. The conference aims to promote the deep integration of deep learning and systems science, providing intelligent solutions for complex problems such as social network analysis, biological network modeling, and transportation system optimization.
IMPORTANT DATES
2026-08-26-Submission Deadline
2026-09-03-Registration Deadline
2026-09-10-Conference Date
About a week after the submission-Notification Date
RECORD
All full paper submissions to the GNNCS 2026 could be written in English and will be sent to at least two reviewers and evaluated based on originality, technical or research content or depth, correctness, relevance to conference, contributions, and readability. All accepted papers of GNNCS 2026 will be published in the conference proceedings, which will be submitted to EI Compendex, Scopus for indexing.
Paper template
Please refer to the paper template for layout
Click to download
Register
All attendees must register in advance to attend the meeting
Consulting service
Submit
Please submit the full text/abstract of the paper to us through the electronic submission system
View more
Call For Papers
Direction 1: Graph Neural Networks Graph Neural Network Architecture Graph Convolutional Network Graph attention mechanism Image Autoencoder Graph generation model Dynamic Graph Neural Network Heterogeneous Graph Neural Networks Interpretability of graph neural networks Graph Neural Network Optimization Image Transfer Learning Direction 2: Complex System Analysis Complex Network Theory network dynamics System emergence behavior Complex System Modeling Multi-agent system Network robustness analysis Cascading failure mechanism Complex system prediction Network Evolution Analysis Complex System Control
......
Indexing Service
Technical Sponsor
Call for Reviewers
As a platform for global academic communication, the quality of conference publication has always an aspect attracting much of our attention. To ensure quality of our publication and to better serve the peers in academic circle, we now call for reviewers among professionals and experts of the world. Professionals and experts who hold PhD (doctoral) degree in the conference related areas are encouraged to join in us and together, we will work hard to become a world-class academic conference. Please send us your CV by email (gnncs@confsflow.com) if you are interested in it.
SCI Journal
Contributors are encouraged to submit papers / abstracts to the conference. The organizing committee will select high-quality papers and recommend them to SCI/SSCI journals. For specific matters, please contact the person in charge of the conferrence.
Submission Guidelines
Delegates are encouraged to submit their papers/abstracts to the conference. Good quality papers will be selected by the organizing committee and Prof. Soteris Kalogirou, the editor in chief. After it, the authors will be invited to extent their papers/abstracts and submit them to gnncs@confsflow.com. The normal size of research papers is 4,000-6,000 words excluding abstract and references.
Submission Portal
If you have any questions or need any help about the conference, please feel free to contact our conference experts on the right:
杨老师
TEL:185 8288 1690
QQ:3965998409
E-mail:gnncs@confsflow.com
About Plagiarism Check
Crosscheck Powered by iThenticate will be used for plagiarism check. The amount of duplication from previously published content should be less than 20%; If the amount of duplication is 20% - 35%, modification maybe required; if the amount of duplication exceeds 35%, the article will be rejected. Please note that there will be no refund for no-shows.