Title: A Primer on Graph Neural Networks for Practitioners in Machine Learning

Abstract: Graph neural networks (GNNs) constitute a class of deep learning architectures specifically engineered to process graph-structured data, where attributes are associated with either nodes or edges. Interest in these models has surged significantly, reflected by a rapid expansion in academic literature driven by their superior performance across diverse applications. This survey elucidates the mechanics of GNNs through an encoder-decoder paradigm, offering concrete examples of decoder applications tailored to various graph analytics objectives. By leveraging theoretical foundations and extensive empirical evaluations on homogeneous graphs, the study examines GNN behavior across varying training scales and levels of graph complexity, highlighting critical challenges such as oversmoothing and oversquashing.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC