ICGT Research Papers
Call for Papers
To foster a lively exchange of perspectives on graph transformation, the programme committee of ICGT 2026 encourages all kinds of papers related to graphs and graph transformation including both theoretical, and application papers.
Important Dates
- Abstracts: 16/02/2026
- Paper Submission: 23/02/2026
- Notification: 06/04/2026
- Final version due: 27/04/2026
- Conference: 29/06/2026–03/07/2026
All deadline end-of-day, AoE
Topics
ICGT welcomes submissions in all topics on graph transformation including, but not limited to:
Theoretical Results on, for example:
- Mathematical models for and approaches to graph transformation
- Analysis and verification of graph transformation systems
- Graph grammars and graph languages
Application of Graph Transformations, for example:
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Formal Methods and Graph Transformation
- Automata on graphs and parsing of graph languages
- Logical aspects of graph transformation
- Computational models based on graphs
- Structuring and modularisation of graph transformation
- Hierarchical graphs and decomposition of graphs
- Parallel, concurrent, and distributed graph transformation
- Term graph and string diagram rewriting
- Bigraphs and bigraphical reactive systems
- Petri nets and other models of concurrency
- Model checking, program analysis and verification, simulation and animation
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Tools and Development environments
- Graph transformation languages and tool support
- Quantum computing, e.g. quantum circuits, graph minor embedding techniques
- Syntax, semantics and implementation of programming languages, including domain-specific and visual languages
- Business process models and notations
- Scene graphs, test environments,test configuration generation
- Graph databases and graph queries, knowledge bases
- Model-driven development and model transformation
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Machine learning and AI, GNNs
- Graph-based machine learning, including graph neural networks and models of rule inference
- Graph transformation and artificial intelligence (e.g., AI for graph transformations, applying graph transformations in AI engineering and search-based software engineering)
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Applications
- Efficient algorithms (e.g. pattern matching, graph traversal, network analysis)
- Applications and case studies in software engineering (e.g. software architectures, refactoring, access control, and service-orientation)
- Applications to computing paradigms (e.g. bio-inspired, quantum, ubiquitous, and visual)
Submission Types
Authors are invited to submit research papers in four possible categories. The accepted papers will be published as LNCS volume. Each paper will be reviewed, single-blind, by at least 3 reviewers
(1) Regular research papers including papers describing applications and case studies. Papers will be evaluated with respect to their originality, significance, and technical soundness. Additional material intended for reviewers (but not publication) may be included in a clearly marked appendix. 16 pages in the LNCS style, excluding references and appendices.
(2) Tool presentation papers, which demonstrate the main features and functionality of graph-based tools. A tool presentation may have an appendix with a detailed demo description which will be reviewed but not included in the proceedings. 8 pages in the LNCS style, excluding references and appendices.
(3) Vision papers, open challenges and representative case studies, reporting on new research directions or open questions and novel ideas which are not yet sufficiently developed to fit in other categories. 8 pages in the LNCS style, excluding references and appendices.
(4) Journal-First allowing for previously published work (in book chapters, journals, or other conferences since 2023) to be presented at ICGT 2026. Up to 2 page extended abstract, LNCS style.
Generative AI Policy
Authors may use AI-generated content in their articles as long as it is disclosed in the article’s Acknowledgments section. The disclosure must state the AI system(s) used, identify the section(s) of the article in which AI was used, and briefly describe the level at which the AI system was used to generate the content.
The use of an AI system for editing and grammar enhancements is a common practice and is outside the scope of the above policies. Disclosure of AI use for editing or grammar enhancements is recommended but not required.