Comprehensive Survey of Graph Neural Networks: 15 Key Review Papers and Resources

Graph Neural Networks (GNNs) have become a central research topic due to the expressive power of graph-structured data. This article presents a curated collection of fifteen recent survey papers that systematically review the development, methods, and applications of GNNs.

The surveys are grouped by themes such as deep learning on graphs, comprehensive overviews of GNN models, gentle introductions, small‑graph versus giant‑network representations, expressive power, relational inductive biases, adversarial attacks and defenses, heterogeneous network representation learning, AutoML for graphs, self‑supervised learning, meta‑learning, and robust graph structure learning.

For each paper, the title, authors, affiliation, publication date, page count, and a direct link to the PDF or arXiv entry are provided. The summaries highlight the main contributions, e.g., classification of GNN architectures (graph recurrent, convolutional, auto‑encoders, reinforcement learning, adversarial methods), benchmark datasets (Cora, Citeseer, Pubmed, Reddit, PPI, etc.), and open research questions such as over‑smoothing in deep GNNs, dynamic graphs, non‑structured scenarios, and scalability.

Additional visual aids (figures and tables) illustrate model categories, aggregator/updater mechanisms, dataset performance comparisons, and the relationship between GNNs and the Weisfeiler‑Lehman test. The article also points to external resources and codebases (PyG, DGL, AliGraph, Euler) for practical implementation.

Overall, this compilation serves as a practical guide for researchers and practitioners seeking a comprehensive understanding of GNN theory, practice, and future directions.