Graph-Based Methods in Artificial Intelligence and Machine Learning, 2nd Edition
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 July 2026 | Viewed by 31146
Special Issue Editors
Interests: knowledge representation; CAD; machine learning; BIM; graph-based computing
Special Issues, Collections and Topics in MDPI journals
Interests: graph grammars; computer-aided graphic design; pattern recognition; diagrammatic reasoning; algorithm analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, graph structures have become an important issue in scientific research and have attracted significant attention in a range of domains. In addition, there is an increasing number of applications for the representation of data by means of well-structured and flexible graph models, mainly due to their ability to encode both topological and semantic information about artefacts. Data can be represented by graphs in many different domains, such as scene graph generation and understanding, object tracking, point cloud classification, proteinomic and genomic data representation, text classification, relationships between documents or words, natural language processing, traffic congestion models, anomalies in networks, buildings in civil engineering, ontologies in different domains, and scenes and action in computer game design.
With these advances, graph structuers have become a new frontier in artificial intelligence and machine learning research. In many of the abovementioned domains, the adoption of graph neural network (GNN) models has been proven to be particularly effective, but other methods in AI and Ml have also been proven to be successful.
For this Special Issue, we invite the submission of papers dealing with both theoretical and applied research. The main subjects include, but are not limited to, the following:
- Graph databases;
- Graph-based versions of classic ML methods;
- Graph neural networks (GNNs);
- Advanced graph models;
- Learning based on graphs;
- Graph data management;
- Graph mining;
- Graph kernels;
- Knowledge graphs;
- Applications of graph models in engineering, computer vusion, graphics, architecture, arts, ecommerce, natural language processing (NLP), computer games, music, etc.
Prof. Dr. Barbara Strug
Prof. Dr. Grażyna Ślusarczyk
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 representation
- GNN
- graph data mining
- graph data management
- graph databases
- graph machine learning
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