Adjacency Matrix

An adjacency matrix is a mathematical representation of a graph, encoding connections between nodes as a square matrix where entries indicate the presence or absence (or strength) of an edge. Current research focuses on developing sophisticated adjacency matrices for various graph types (directed, signed, dynamic, functional) and employing them within machine learning models, particularly graph neural networks (GNNs), for tasks like node classification, link prediction, and network inference. These advancements improve the accuracy and efficiency of GNNs, particularly in handling complex, noisy, or high-dimensional graph data. The resulting improvements have significant implications across diverse fields, including brain network analysis, traffic flow prediction, and drug discovery.