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Introduction to Graph Neural Networks for Machine Learning Engineers

arXiv 2412.19419 ↗First posted 27 Dec 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Graph neural networks are deep neural networks designed for graphs with attributes attached to nodes or edges. The number of research papers in the literature concerning these models is growing rapidly due to their impressive performance on a broad range of tasks. This survey introduces graph neural networks through the encoder-decoder framework and provides examples of decoders for a range of graph analytic tasks. It uses theory and numerous experiments on homogeneous graphs to illustrate the behavior of graph neural networks under different training sizes and degrees of graph complexity, with an emphasis on oversmoothing and oversquashing.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 81 of its 142 references that are in the map. First posted on arXiv 27 Dec 2024.

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81 of 142 references.

References · 81

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38 more
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