arXiv:2412.19419v2 Announce Type: replace Abstract: 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
The rapid growth in Graph Neural Networks (GNNs) research and applications necessitates foundational texts to onboard new machine learning engineers.
The increasing sophistication and adoption of GNNs will empower advanced AI applications critical for various industries, making it a key component of future AI development.
The availability of accessible educational resources like this survey means more engineers can enter GNN development, accelerating innovation and deployment in graph-based AI problems.
- · AI/ML Engineers
- · Data Scientists
- · Software Companies
- · Research Institutions
Increased number of practitioners skilled in Graph Neural Networks.
Faster development and deployment of AI solutions for complex relational data.
New classes of AI applications emerging from widespread GNN expertise, potentially impacting scientific discovery and cybersecurity.
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