Subgraph Counting

Subgraph counting aims to efficiently determine the number of occurrences of a specific pattern (subgraph) within a larger graph, a fundamental problem across diverse fields like network analysis and bioinformatics. Current research focuses on improving the computational efficiency of subgraph counting algorithms, particularly through the development of novel matrix-based formulas, optimized hash functions for privacy-preserving distributed computation, and the application of graph neural networks (GNNs), including localized versions and those incorporating subgraph-level information. These advancements are crucial for handling increasingly large and complex graph datasets, enabling more efficient analysis and unlocking new insights in various scientific and practical applications.