3rd CFP: Special Issue on Knowledge Discovery from Graphs - Springer Data Mining and Knowledge Discovery (Q1)

[Mirko Marras]

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# Call for Papers #

Special Issue on Knowledge Discovery from Graphs

Springer Data Mining and Knowledge Discovery Journal

https://link.springer.com/journal/10618/updates/27773304

# Important Dates #

• 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

Submissions that are received before the first deadline will be directly sent out for review.

# Introduction #

The rapidly advancing research on Knowledge Discovery from Graphs (KDG) highlights the growing adoption of graph data structures. Representing information as nodes interconnected by diverse relationships enables the extraction of rich features and the inference of actionable insights. This special issue seeks to bring together research spanning various domains where the use of graph data drives advancements in data mining and knowledge discovery. As graphs continue to revolutionize numerous fields, this special issue aims to attract a broad and diverse group of stakeholders, including researchers, developers, and practitioners. By doing so, it also promotes cross-disciplinary and interdisciplinary dialogues, addressing the pervasive influence of KDG across a wide range of disciplines.

The primary objective of this special issue is to provide researchers with a dedicated platform to share their studies on graph data and graph-based technologies. This addresses the notable absence of a focused venue within the data mining and knowledge discovery community, despite the increasing attention these topics have received. The issue aims to foster a collaborative environment that advances research on leveraging graphs, not only as a powerful analytical tool but also as a means to showcase the unique benefits interconnected networks offer compared to other data structures.

This special issue of Springer Data Mining and Knowledge Discovery (DMKD) welcomes submissions that demonstrate the cutting-edge adoption of graph data and technologies in real-world applications, propose novel theoretical frameworks for knowledge extraction from graphs, and explore additional dimensions of graph-based algorithms, including responsible AI.
Submissions may include original research articles, case studies, and surveys that advance the state of the art in KDG.

# Topics #

We invite submissions on a range of topics:

Algorithm Design and Graph Representations

• Novel algorithms for scalable graph mining and analysis.
• Advances in graph embeddings and graph representation learning (e.g., GNNs).
• Efficient processing of large-scale, heterogeneous, and dynamic graphs.
• Integration of temporal and spatial information in graph models.
• Graph kernels, summarization/coarsening, alignment.
• Graph language, generative, and foundation models.

Evaluation and Benchmarks

• New metrics and benchmarks for graph mining and learning methods.
• Empirical evaluations of graph-based systems in real-world scenarios.
• Analysis of robustness and reliability in graph-based decision systems.

Applications of Knowledge Discovery from Graphs

• Real-world case studies in social media analysis (e.g., misinformation propagation), recommender systems, and computer vision.
• Knowledge graph construction and its use in information retrieval and natural language processing (e.g., retrieval augmented generation with graphs).
• Applications in financial security (e.g., fraud detection), cybersecurity (e.g., malware detection/propagation), and graph ML platforms (e.g., in-database machine learning).
• Use of graph-based techniques in bioinformatics (e.g., drug discovery), transportation/mobility networks (e.g., traffic prediction), and climate science (e.g., global weather forecasting).

Beyond Accuracy in Knowledge Discovery from Graphs

• Interpretable and explainable graph-based methodologies.
• Robustness and adversarial attacks on graphs.
• Responsible AI (e.g., fairness, bias) on graph neural networks.
• Generalization of graph-based approaches on unseen nodes and graph structures.

Emerging Trends and Interdisciplinary Approaches

• Fusion of graph learning with other machine learning paradigms (e.g., federated learning, reinforcement learning).
• Use of knowledge discovery techniques in dynamic and evolving graphs.
• Cross-disciplinary approaches combining KDG with fields like neuroscience, urban planning, and environmental science.

We welcome original research papers, case studies, and review articles that contribute to the body of knowledge in these areas.

# Guest Editors #

• Ludovico Boratto, University of Cagliari (Italy), ludovico...@acm.org
• Daniele Malitesta, Université Paris-Saclay, CentraleSupélec, Inria (France), daniele....@centralesupelec.fr
• Fragkiskos D. Malliaros, Université Paris-Saclay, CentraleSupélec, Inria (France), fragkiskos...@centralesupelec.fr
• Mirko Marras, University of Cagliari (Italy), mirko....@acm.org
• Giacomo Medda, University of Cagliari (Italy), giacom...@unica.it
• Cataldo Musto, University of Bari Aldo Moro (Italy), catald...@uniba.it
• Erasmo Purificato, European Commission, Joint Research Centre (Italy), erasmo.p...@acm.org

--

Mirko Marras (he/him) | Ph.D. 

Tenure-Track Assistant Professor (RTT)

University of Cagliari | Dept. of Mathematics and Computer Science

Palazzo delle Scienze, Via Ospedale 72 (Google Maps)

09124 Cagliari | Italy

 

E-mail: mirko....@unica.it | mirko....@acm.org

Website: unica.it/unica/page/en/mirko_marras | mirkomarras.com

LinkedIn: mirkomarras | Twitter: @mirkomarras 

Google Scholar: JZhqKBIAAAAJ | Scopus: 57193883534