Maximum Common Subgraph

Maximum Common Subgraph (MCS) problems aim to identify the largest shared structure between two or more graphs, a fundamental task with applications in diverse fields like anomaly detection and graph retrieval. Current research emphasizes developing efficient algorithms and neural network architectures, including those based on contrastive learning and graph neural networks, to approximate or exactly solve MCS, often focusing on improving speed and accuracy for large graphs. These advancements are crucial for improving the scalability and interpretability of graph-based analyses across various domains, from industrial monitoring to optimizing deep learning model compilation.