Overview
This editorial announces a call for papers for a special issue focused on Knowledge Discovery from Graphs (KDG) in the esteemed Springer Data Mining and Knowledge Discovery Journal. The issue aims to highlight the increasing importance of graph data structures in various domains, fostering collaboration among researchers, developers, and practitioners.
Background & Relevance
The field of Knowledge Discovery from Graphs is rapidly evolving, driven by the growing utilization of graph data structures that represent information as interconnected nodes. This approach allows for the extraction of significant features and actionable insights, making it a vital area of study in data mining. As graphs continue to transform numerous sectors, this special issue serves as a platform for sharing innovative research and methodologies that leverage graph data effectively.
Key Details
- Submissions Open: June 9, 2025
- Submissions Close: October 13, 2025
- First-Round Review Decisions: January 19, 2026
- Deadline for Revised Submissions: April 13, 2026
- Notification of Final Decisions: June 8, 2026
- Journal: Springer Data Mining and Knowledge Discovery
- Link: Springer DMKD Journal
Eligibility & Participation
This call for papers is open to researchers and practitioners in the field of data mining and knowledge discovery, particularly those focusing on graph data and technologies. Contributions from diverse domains are encouraged to promote interdisciplinary dialogue and innovation.
Submission or Application Guidelines
To submit your work, please follow these steps:
1. Prepare your manuscript according to the journal’s submission guidelines.
2. Ensure your research aligns with the topics outlined in this call.
3. Submit your manuscript through the journal’s online submission system before the closing date.
4. Papers submitted before the first deadline will be sent out for review promptly.
More Information
The integration of graph data into various applications is reshaping the landscape of data analysis and knowledge extraction. This special issue aims to gather cutting-edge research that not only advances theoretical frameworks but also showcases practical applications of graph technologies. Topics of interest include algorithm design, evaluation benchmarks, and the exploration of responsible AI practices in graph-based methodologies.
Conclusion
Researchers are encouraged to explore this opportunity to contribute to the growing body of knowledge in Knowledge Discovery from Graphs. Submissions that highlight innovative approaches and real-world applications will be particularly valued. Interested parties should prepare their manuscripts for submission by the specified deadlines and share this call with colleagues in the field.
Category: CFP & Deadlines
Tags: knowledge discovery, graphs, data mining, machine learning, algorithm design, graph representation, applications, responsible AI, interdisciplinary, graph neural networks
