Overview
This editorial highlights a call for papers for a special issue focused on Knowledge Discovery from Graphs (KDG) in the Springer journal, Data Mining and Knowledge Discovery. This initiative is significant as it addresses the growing importance of graph data structures in various research domains, providing a platform for researchers to share their findings and foster interdisciplinary collaboration.
Background & Relevance
Knowledge Discovery from Graphs is an emerging field that leverages the relationships between data points represented as nodes in a graph. This approach allows for the extraction of valuable insights and features, making it increasingly relevant in areas such as social media analysis, cybersecurity, and bioinformatics. As the use of graph data continues to expand, this special issue aims to consolidate research efforts and promote dialogue among scholars and practitioners.
Key Details
- Submission Open: June 9, 2025
- Submission 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
- Link: Springer DMKD Journal
Eligibility & Participation
This call invites contributions from researchers, developers, and practitioners interested in the application of graph data and technologies. It targets a wide audience across various disciplines, encouraging submissions that advance the state of the art in KDG.
Submission or Application Guidelines
Interested authors are encouraged to submit original research articles, case studies, and reviews that explore the following topics:
– Novel algorithms for scalable graph mining
– Advances in graph representation learning
– Evaluation metrics for graph-based systems
– Applications in social media, finance, and bioinformatics
– Interdisciplinary approaches combining KDG with other fields
Submissions that arrive before the first deadline will be prioritized for review.
Additional Context / Real-World Relevance
The integration of graph structures in data analysis is transforming how insights are derived across various sectors. By focusing on graph data, this special issue not only highlights current advancements but also sets the stage for future research directions that can lead to innovative solutions in real-world applications.
Conclusion
Researchers are encouraged to explore this opportunity to contribute to the evolving field of Knowledge Discovery from Graphs. This special issue aims to gather diverse perspectives and foster collaboration, ultimately enriching the body of knowledge in data mining and knowledge discovery. Interested parties should prepare their submissions in accordance with the provided guidelines and deadlines.
Category: CFP & Deadlines
Tags: knowledge discovery, graphs, data mining, machine learning, algorithm design, applications, evaluation, interdisciplinary, responsible AI
