LGAIMar 18, 2024

Graphs Unveiled: Graph Neural Networks and Graph Generation

arXiv:2403.13849v11 citationsh-index: 1Prod Syst Inf Eng
Originality Synthesis-oriented
AI Analysis

It summarizes existing research on GNNs for readers interested in graph data challenges, but it is incremental as it does not introduce new methods or results.

This paper is a survey that provides a comprehensive overview of Graph Neural Networks (GNNs), discussing their applications across various domains and presenting graph generation as an advanced field.

One of the hot topics in machine learning is the field of GNN. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. This paper represents a survey, providing a comprehensive overview of Graph Neural Networks (GNNs). We discuss the applications of graph neural networks across various domains. Finally, we present an advanced field in GNNs: graph generation.

Foundations

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