Advances in Graph Data Mining and Complex Network Analysis
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 9
Special Issue Editors
Interests: social network analysis; complex network analysis; network science; graph theory; criminal networks; street/road networks; network construction; network robustness; link prediction; centrality metrics
Interests: network science; criminal networks; machine learning; data science; social network analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Graph data mining and complex network analysis have become essential tools for understanding the interconnected structures underpinning modern scientific, technological, and social systems. With the rapid growth of graph-structured data across domains, including social and communication networks, biological systems, transportation infrastructures, knowledge graphs, cybersecurity, IoT ecosystems, and criminal networks, there is a pressing need for innovative computational methods capable of extracting meaningful patterns, predicting dynamics, and supporting informed decision making. This Special Issue aims to present cutting-edge research contributions that advance the theoretical foundations, algorithmic developments, and practical applications of graph data mining and network science. Topics of interest include novel methods for graph representation learning, scalable graph algorithms, multilayer network analysis, community detection, anomaly and link prediction, network robustness, and interpretability in graph-based models. Application-driven studies are equally welcome, particularly those demonstrating the impact of graph analytics in domains such as healthcare, bioinformatics, mobility analysis, sustainability, cybersecurity, social media intelligence, and the modeling and disruption of criminal networks. By bringing together interdisciplinary perspectives, this Special Issue seeks to highlight emerging trends, foster collaboration among researchers, and promote next-generation methodologies for analyzing, modeling, and understanding complex networks in real-world environments.
Dr. Annamaria Ficara
Dr. Giacomo Fiumara
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- graph data mining
- complex network analysis
- graph representation learning
- network modeling
- community detection
- link prediction
- graph neural networks (GNNs)
- multilayer networks
- network anomaly detection
- criminal network analysis
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
