GNN Graph Nural network (General overview) скачать в хорошем качестве

GNN Graph Nural network (General overview)

GNN typically refers to Graph Neural Networks. These are a class of neural networks designed to process data that is structured as graphs. Graphs consist of nodes (representing entities or objects) and edges (representing relationships or connections between the nodes). Graph neural networks leverage this structure to perform tasks such as node classification, link prediction, graph classification, and graph generation. Here are some key components and concepts related to Graph Neural Networks (GNNs): 1. **Node Features**: Each node in a graph can have associated features, which can represent attributes or properties of the node. 2. **Edge Features**: Similarly, edges in a graph can have associated features, representing properties of the relationships between nodes. 3. **Graph Convolutional Layers**: These are the building blocks of GNNs, analogous to convolutional layers in image processing neural networks. Graph convolutional layers aggregate information from neighboring nodes and update node features based on this aggregated information. 4. **Aggregation Functions**: GNNs typically use aggregation functions (such as mean, sum, or max pooling) to combine information from neighboring nodes during message passing. 5. **Message Passing**: This is the process through which information is exchanged between neighboring nodes in the graph. It involves passing messages along edges and updating node representations based on these messages. 6. **Graph Pooling**: Graph pooling layers down-sample the graph, reducing its size while retaining important structural information. This is analogous to pooling layers in convolutional neural networks for images. 7. **Graph Attention Mechanisms**: These mechanisms allow GNNs to assign different importance weights to neighboring nodes during message passing, enabling them to focus on more relevant nodes. 8. **Graph Embeddings**: GNNs can learn low-dimensional representations (embeddings) of nodes or entire graphs, which capture structural and semantic information. Graph Neural Networks have found applications in various domains, including social network analysis, recommendation systems, bioinformatics, and drug discovery, among others. They offer a powerful framework for modeling and analyzing complex relational data. References: Hamilton, William L. Graph representation learning. Morgan & Claypool Publishers, 2020. Zhou, Jie, et al. "Graph neural networks: A review of methods and applications." AI open 1 (2020): 57-81. Jin, Zhihua, et al. "Gnnlens: A visual analytics approach for prediction error diagnosis of graph neural networks." IEEE Transactions on Visualization and Computer Graphics (2022). Youtub    • Intro to Graph Neural Networks in Arabic |...      • Graph Neural Networks      • Stanford CS224W: Machine Learning with Graphs