Graph Pattern

Graph pattern mining focuses on identifying recurring structures within complex graph data, aiming to extract meaningful insights and improve efficiency in various applications. Current research emphasizes developing efficient algorithms, such as those based on the Weisfeiler-Leman test and Minimum Description Length principle, and leveraging graph neural networks (GNNs) for pattern discovery and representation learning, particularly in dynamic and open-world scenarios. These advancements are crucial for analyzing large-scale datasets in diverse fields, including brain network analysis, knowledge graph reasoning, and fault diagnosis in electronic systems, enabling more effective data analysis and improved decision-making.