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This article provides an introductory overview of graph neural networks, explaining their motivation, basic concepts, and detailing classic models such as GCN, GraphSAGE, GAT, Graph Auto‑Encoder, and DiffPool, along with their advantages, limitations, and experimental results on various benchmark datasets.
Graph Neural Networks (GNNs) have become a research hotspot, and this article provides an intuitive overview of classic GNN models such as GCN, GraphSAGE, GAT, graph auto‑encoders, and DiffPool, discussing their architectures, advantages, limitations, and experimental results across various benchmark datasets.
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