Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. [1][2][3][4][5] One prominent example is molecular drug design. [6][7][8] Each input sample is a graph representation of a molecule, where atoms form the nodes and chemical bonds between atoms form the edges.

2022年7月21日 · What is a Graph Neural Network (GNN)? Graph Neural Networks are special types of neural networks capable of working with a graph data structure. They are highly influenced by Convolutional Neural Networks (CNNs) and graph embedding. GNNs are used in predicting nodes, edges, and graph-based tasks. CNNs are used for image classification ...

2020年1月1日 · Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks.

GNN fundamentals, modeling, applications, complexity, algorithms, accelerators & data flows: A review of the field of GNNs is presented from a computing perspective. A brief tutorial is included on GNN fundamentals, alongside an in-depth analysis of acceleration schemes, culminating in a communication-centric vision of GNN accelerators.

2021年9月2日 · A GNN is an optimizable transformation on all attributes of the graph (nodes, edges, global-context) that preserves graph symmetries (permutation invariances).

受到 pytorch_geometric 的启发，我们开发了基于Tensorflow的GNN库: tf_geometric，其中集成了高效且对科研工作者友好的GNN模型。 GNN-algorighms则是基于tf_geometric对近几年如火如荼的图神经模型进行详述以及手把手教大家实现GNN模型的教程。 本人才疏学浅，如教程有任何不妥或者错误之处，欢迎扫描下方的二维码加群或者开issue讨论。 同时本群也欢迎志同道合之士加群共同探讨图神经网络。 AI恒久远，GNN永流传。 （由于GNN-algorithms交流群人数已经超 …

2025年3月4日 · Graph Neural Networks (GNNs) are a class of neural networks designed specifically to work with graph-structured data. They’re used to learn patterns and relationships between connected entities within a graph, making them ideal for applications like social networks, recommendation systems, and molecular studies. How GNNs Process Graph Data?

gnn模型详述. gnn模型基于 信息传播机制 ，每一个节点通过相互交换信息来更新自己的节点状态，直到达到某一个稳定值，gnn的输出就是在每个节点处，根据当前节点状态分别计算输出。有如下定义：

Step-wise explanations of the mechanisms that underpin specific classes of GNNs, as enu-merated in Table 1. These explanations progressively build a holistic understanding of GNNs. Descriptions of the advantages and disadvantages of GNNs, and key areas of application. Full examples of how specific GNN variants can be applied to real world problems.

2024年5月30日 · Graph Neural Networks (GNNs) are a class of artificial neural networks designed to process data that can be represented as graphs. Unlike traditional neural networks that operate on Euclidean data (like images or text), GNNs are tailored to handle non-Euclidean data structures, making them highly versatile for various applications.