Induced Subgraphs

Induced subgraphs, subsets of vertices and their connecting edges within a larger graph, are central to numerous graph-related problems. Current research focuses on efficiently identifying important induced subgraphs, particularly for explaining the predictions of graph neural networks (GNNs) and solving optimization problems like finding the densest subgraph. This involves developing novel algorithms, such as those employing edge-based induction or quantum computing approaches, to improve efficiency and scalability. The findings have significant implications for advancing GNN interpretability, enhancing the performance of combinatorial optimization algorithms, and providing solutions for various applications in network analysis and beyond.