At the beginning of the year, I felt that Graph Neural Network (GNN) became a buzzword.Here I suggest taking a look at the top applications of GNN that we have recently.
Deep learning cannot make causal reasoning, and graph model (GNN) is one of the solutions. Professor Sun Maosong of Tsinghua University published a review paper, comprehensively expounded GNN and its methods and applications, and proposed a unified representation that can characterize the propagation steps in various GNN models. In the text chart, it is recommended to print the high-definition plastic stickers for reference.
Graph neural network (Graph NN) is a recent research hotspot, especially the "Graph Networks" proposed by DeepMind, which is expected to enable deep learning to achieve causal reasoning. However, this paper is difficult to understand. Dr. Deng Wei, the chief AI scientist of Fosun Group and the founder of DaDian Medical, analyzed the significance of DeepMind “Figure Network” based on the clear classification of GNN review by Professor Yu Shilun of Tsinghua University.
Recently, graph neural networks (GNN) have become increasingly popular in various fields, including social networks, knowledge maps, recommendation systems, and even life sciences. GNN's ability to model the dependencies between nodes in a graph has made a breakthrough in the research field related to graph analysis. This article aims to introduce the basics of graph neural networks and two more advanced algorithms: DeepWalk and GraphSage.