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Graph Neural Networks: A Foundational Guide for the Applied ML Engineer
Issue Vol. 2 No. 02 (2025): Volume 02 Issue 02 --- Section Articles
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Abstract
Graph Neural Networks (GNNs) have emerged as a powerful class of deep learning models designed to handle the complexities of graph-structured data. Their ability to learn from relational information has led to state-of-the-art performance on a wide array of tasks, from social network analysis to molecular chemistry. However, for machine learning engineers new to this domain, the steep learning curve and the sheer variety of GNN architectures can be daunting. This article provides a comprehensive introduction to GNNs, grounding the discussion in the intuitive encoder-decoder framework. We focus on three foundational GNN architectures—Graph Convolutional Networks (GCN), GraphSAGE, and Graph Attention Networks (GATv2)—to build a concrete understanding of their mechanisms. Through a series of extensive experiments on thirteen homogeneous graph datasets, we systematically investigate how GNN performance is influenced by fundamental graph properties, particularly homophily, and varying training conditions. We compare these models against established baselines, including Multilayer Perceptrons (MLP) and DeepWalk, to highlight the unique advantages of GNNs. Our findings reveal that architectural choices are critical; more flexible models like GraphSAGE excel on low-homophily graphs, whereas simpler, more rigid models like GCN are highly effective on high-homophily graphs, especially in low-data regimes. Furthermore, we demonstrate that hyperparameter tuning offers the most significant performance gains in moderately difficult learning scenarios. By combining theoretical explanations with empirical evidence and qualitative analyses of the GNN learning process, this work serves as a practical and accessible starting point for engineers looking to effectively develop and deploy GNNs.
Keywords
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