Call for papers at 5th GCLR workshop @AAAI 2026 [Deadline Extended]
94 views
Skip to first unread message
Tarun Kumar
Oct 13, 2025, 12:58:39 PMOct 13
to ai-in...@googlegroups.com
Dear all,
The 5th Workshop on Graphs and more Complex structures for Learning and Reasoning (GCLR) will be held in conjunction with AAAI 2026.
Website: https://sites.google.com/view/gclr2026/
Website: https://sites.google.com/view/gclr2026/
Submission portal: https://cmt3.research.microsoft.com/GCLR2026/Submission/Index
Important Dates
- Poster/Short/Position Paper Submission Deadline: Oct
1522, 2025 (Pacific Time) - Full Paper Submission Deadline: Oct
1522, 2025 (Pacific Time) - Paper Notification: Nov 5, 2025
Call for submissions:
We invite submissions from participants who can contribute to the theory and applications of modeling complex graph structures, such as hypergraphs, multilayer networks, and knowledge graphs, with their applications to foundational models and trustworthy AI. The topics of interest include, but are not limited to, the following:
We invite submissions from participants who can contribute to the theory and applications of modeling complex graph structures, such as hypergraphs, multilayer networks, and knowledge graphs, with their applications to foundational models and trustworthy AI. The topics of interest include, but are not limited to, the following:
- Fairness-aware Learning in Complex Graphs
- Benchmarking Foundation Models with Complex Data
- Privacy Preservation in Complex Graphs
- Causal Inference and Complex Networks
- Knowledge Graph-enhanced Foundation Models
- Theoretical analysis of graph algorithms or models
- Network representation learning and manifold embedding methods
- Optimization methods for graphs/manifolds
- Link analysis/prediction, node classification, clustering for complex graph structures
- Probabilistic and graphical models for structured data
- Knowledge graph construction
- Social network analysis and measures
- Constraint satisfaction and programming (CP), (inductive) logic programming (LP and ILP)
Submission Guidelines
We welcome submissions in the following two formats:
Accepted submissions will have the option of being published on the workshop website. For authors who do not wish their papers to be posted online, please mention this in the workshop submission. The submissions need NOT be anonymized.
- Poster/Short/Position paper: We encourage participants to submit preliminary but interesting ideas that have not been published before. These submissions would benefit from additional exposure and discussion that can shape a better future publication. We also invite papers that have been published at other venues to spark discussions and foster new collaborations. Submissions may consist of up to 4 pages plus one additional page solely for references.
- Full papers: Submissions must represent original material that has not appeared elsewhere for publication, and that is not under review for another refereed publication. Submissions may consist of up to 7 pages of technical content plus up to two additional pages solely for references.
Accepted submissions will have the option of being published on the workshop website. For authors who do not wish their papers to be posted online, please mention this in the workshop submission. The submissions need NOT be anonymized.
About the workshop:
In today's rapidly evolving technological landscape, we confront the intricate challenges of complex systems head-on. Graph-based modeling often fails to capture the inherent complexities of such systems and then we move on to a diverse array of complex graph structures: knowledge graphs, attributed graphs, multilayer graphs, hypergraphs, and more. These structures provide more accurate representations for these intricate systems.
In the midst of this complexity, the importance of trustworthy AI, particularly in foundational model research, cannot be overstated. Ensuring ethical, explainable, and fair AI aligns perfectly with the nuances of complex systems. Trustworthy AI hinges on our ability to understand and make transparent AI algorithms that grapple with intricate interactions within these systems. Simultaneously, the reliability of foundation models plays a pivotal role in various AI applications reliant on complex graph-based data.
This workshop aims to bring researchers from these diverse but related fields together and embark interesting discussions on new challenging applications that require complex system modeling and discovering ingenious reasoning methods. We have invited several distinguished speakers with their research interest spanning from the theoretical to experimental aspects of complex networks.
In the midst of this complexity, the importance of trustworthy AI, particularly in foundational model research, cannot be overstated. Ensuring ethical, explainable, and fair AI aligns perfectly with the nuances of complex systems. Trustworthy AI hinges on our ability to understand and make transparent AI algorithms that grapple with intricate interactions within these systems. Simultaneously, the reliability of foundation models plays a pivotal role in various AI applications reliant on complex graph-based data.
This workshop aims to bring researchers from these diverse but related fields together and embark interesting discussions on new challenging applications that require complex system modeling and discovering ingenious reasoning methods. We have invited several distinguished speakers with their research interest spanning from the theoretical to experimental aspects of complex networks.
Attendance
Attendance is open to all. For registration, please refer to the registration guidelines by AAAI-26. Accepted papers will be presented in poster format and some will be selected for oral presentation. At least one author of each accepted submission must be present at the workshop.
Organizing Committee:
- Organizers:
- Deepak Maurya (Purdue University)
- Gayathri Saranathan (Hewlett Packard Labs)
- Tarun Kumar (Hewlett Packard Labs)
- Advisors:
- Balaraman Ravindran (IIT Madras)
- Ginestra Bianconi (Queen Mary University of London)
Reply all
Reply to author
Forward
0 new messages