Overview
The 5th Workshop on Graphs and more Complex Structures for Learning and Reasoning (GCLR) is set to take place in conjunction with AAAI 2026. This workshop aims to gather researchers interested in the theoretical and practical aspects of modeling complex graph structures, which are increasingly relevant in AI and machine learning applications.
Background & Relevance
In recent years, the significance of complex systems has surged, particularly in AI research where graph-based models are essential for capturing intricate relationships. The workshop will explore various graph structures, including hypergraphs and multilayer networks, and their applications in foundational models and trustworthy AI. This focus is crucial as the demand for ethical and explainable AI continues to grow, necessitating robust methodologies for understanding complex interactions within data.
Key Details
- Event: 5th GCLR Workshop
- Date: In conjunction with AAAI 2026
- Submission Deadlines:
- Poster/Short/Position Paper: October 15, 2025 (Pacific Time)
- Full Paper: October 15, 2025 (Pacific Time)
- Notification of Acceptance: November 5, 2025
- Submission Portal: CMT Submission Portal
- Workshop Website: GCLR 2026
Eligibility & Participation
The workshop invites contributions from researchers, practitioners, and students who are engaged in the study of complex graph structures. Submissions are encouraged from those who can offer insights into the theory and applications of these models, particularly in relation to foundational AI models.
Submission or Application Guidelines
Submissions are accepted in two formats:
– Poster/Short/Position Papers: These can include preliminary ideas or previously published works intended to foster discussion. Submissions should be up to 4 pages, with an additional page for references.
– Full Papers: Original research that has not been published elsewhere is welcomed. These papers may consist of up to 7 pages of content and 2 pages for references. All submissions must adhere to the AAAI paper guidelines.
Accepted papers will have the opportunity to be published on the workshop website, and authors can choose not to have their papers posted online if they prefer.
Additional Context / Real-World Relevance
The exploration of complex graph structures is vital for advancing AI methodologies, particularly in areas such as social network analysis, causal inference, and privacy preservation. As AI systems become more integrated into various sectors, understanding the underlying graph-based data structures will be crucial for developing trustworthy and effective AI solutions.
Conclusion
The GCLR workshop at AAAI 2026 presents a valuable opportunity for researchers to engage with peers, share innovative ideas, and contribute to the evolving field of complex graph structures in AI. Interested participants are encouraged to prepare their submissions and join the discussions that will shape the future of this important area of research.
Category: CFP & Deadlines
Tags: graphs, complex structures, trustworthy ai, knowledge graphs, aaai, machine learning, network analysis, causal inference, fairness-aware learning, multilayer networks
