Michael Anderson

GRAPH NEURAL NETWORKS: FUNDAMENTALS, APPLICATIONS, AND RESEARCH DIRECTIONS

Graph Neural Networks (GNNs) have emerged as an important class of deep learning models for analyzing graph-structured data. Unlike conventional neural networks that primarily operate on regular grids or sequential inputs, GNNs learn representations by combining information about individual entities and their relations...