Theories and Applications on Data Mining in Graph Neural Networks

Dear Colleagues,

Graph neural networks (GNNs) have emerged as a powerful paradigm for learning representations from graph-structured data, enabling breakthroughs across a wide range of domains including social network analysis, molecular property prediction, recommendation systems, natural language processing and computer vision. The intersection of data mining with GNNs offers transformative opportunities to extract meaningful patterns, relationships and knowledge from complex, interconnected datasets that traditional approaches cannot adequately capture.

This Special Issue aims to bring together cutting-edge theoretical advances and practical applications at the confluence of data mining and graph neural networks. We invite original research articles, comprehensive reviews and case studies that address fundamental challenges such as the scalability of GNN-based mining algorithms, the interpretability of learned graph representations, the robustness and generalization of GNN models and novel architectures tailored for specific data mining tasks.

By consolidating recent advances and fostering dialogue between theorists and practitioners, this Special Issue seeks to advance the state of the art and identify promising future research directions in GNN-based data mining. This Special Issue welcomes submissions on, but not limited to, the following topics:

• Novel GNN architectures for graph-based data mining tasks
• Scalable graph neural network algorithms for large-scale data
• Graph-based anomaly and fraud detection
• Knowledge graph construction, completion and reasoning with GNNs
• Dynamic and temporal graph mining using neural approaches
• Heterogeneous graph learning and multi-relational data mining
• Explainability and interpretability of GNN-based data mining models
• GNN-driven recommendation systems and link prediction
• Graph representation learning for molecular and biological data
• Applications of GNNs in social network analysis and community detection
• Transfer learning and domain adaptation in graph neural networks
• Benchmark datasets and evaluation methodologies for GNN-based mining

We look forward to receiving your contributions. 

Dr. Vikas Palakonda
Dr. Samira Ghorbanpour
Guest Editors

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• graph neural networks
• data mining
• graph representation learning
• knowledge graphs
• anomaly detection
• dynamic graph learning
• heterogeneous graphs
• scalable graph algorithms
• graph-based recommendation systems
• explainable GNNs