Graph-Based Machine Learning Techniques

Dear Colleagues,

We invite submissions to a Special Issue on Graph-Based Machine Learning (GML), focusing on advances that leverage relational structures to learn from complex, interconnected data. Graphs pervade modern data ecosystems—knowledge graphs, molecular and protein interaction networks, transportation and power grids, and social and financial networks—where performance hinges on modeling entities and their relationships. Recent progress in graph neural networks and related methods has unlocked powerful capabilities for reasoning, prediction, and generation, yet key challenges remain in scalability, heterogeneity, dynamics, reliability, and responsible deployment. This Special Issue aims to consolidate cutting-edge theory, methods, systems, and applications that push GML toward practical, trustworthy, and impactful use.

As AI systems move beyond i.i.d. samples to structured, multi-relational settings, GML provides the inductive biases and operators needed for generalization under distribution shifts, compositional reasoning, and multi-hop inference. The rapid convergence of GML with foundation models (e.g., graph-augmented RAG, neurosymbolic integration, tool-using agents), the rise of dynamic and heterogeneous graphs, and the demand for transparent, fair, robust, and private models underscore the urgency and importance of this research direction across science and industry.

Topics of interest include, but are not limited to, the following:

• Novel GNN architectures; self-/semi-supervised, contrastive, and transfer learning on graphs;
• Dynamic, temporal, heterogeneous, attributed, and hypergraph learning;
• Graph generative models (e.g., molecules, materials, program graphs) and structure-aware diffusion;
• Scalability: billion-edge graphs, streaming/incremental learning, distributed and efficient systems;
• Explainability, fairness, robustness, privacy, and security of graph models; adversarial graphs;
• Out-of-distribution generalization, causal and counterfactual reasoning on graphs;
• Foundation models with graphs: graph-augmented RAG, graph prompting, LLM–graph integration, neurosymbolic methods;
• Evaluation: benchmarks, metrics, reproducibility, compute reporting, and open resources;
• Applications in biomedicine and drug discovery, recommender systems, finance and fraud, mobility and logistics, cybersecurity, climate and energy, and scientific discovery.

Prof. Dr. Zsolt Tibor Kosztyán
Guest Editor

Manuscript Submission Information

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