作者将该方法命名为 Graph-less Neural Network (GLNN)。具体来说,GLNN 是一个从 Teacher GNN 到 Student MLP 的 KD 建模范式,最终 GLNN 是一个通过 KD 优化的 MLP,其在训练中具备图上下文感知能力,但在推理中不依赖于图。 分析表明,GLNNs work 的关键因素是 large MLP 以及节点特征和标签之间的互信息关...
为了结合GNN和MLP的优点来搭建一个高准确率且低延迟的模型,这篇文章提出了一个模型叫做Graph-less Neural Network (GLNN)。具体来说,GLNN是一种涉及从教师GNN到学生MLP的知识蒸馏(knowledge distillation)模型。经过训练后的学生MLP即为最终的GLNN,所以GLNN在训练中享有图拓扑结构(graph topology)的好处,但在推理中...
Method 为了结合 GNN 和 MLP 的优点来搭建一个高准确率且低延迟的模型,这篇文章提出了一个模型叫做 Graph-less Neural Network(GLNN)。具体来说,GLNN 是一种涉及从教师 GNN 到学生 MLP 的知识蒸馏(knowledge distillation)模型。经过训练后的学生 MLP 即为最终的 GLNN,所以 GLNN 在训练中享有图拓扑结构(grap...
因此,本文试图沿着图神经网络的历史脉络,从最早基于不动点理论的图神经网络(Graph Neural Network, GNN)一步步讲到当前用得最火的图卷积神经网络(Graph Convolutional Neural Network, GCN), 期望通过本文带给读者一些灵感与启示。 本文的提纲与叙述要点主要参考了2篇图神经网络的Survey,分别是来自IEEE Fellow的 A Co...
For a node vv in graph GG , the K−hopK−hop neighbors NK,spdv,GNv,GK,spd of vv based on shortest path distance kernel is the set of nodes that have the shortest path distance from node vv less than or equal to KK . We further denote Qk,spdv,GQv,Gk,spd as the set of ...
Graph Neural Network Library for PyTorch. Contribute to pyg-team/pytorch_geometric development by creating an account on GitHub.
The Neural Equivariant Interatomic Potential (NequIP25) predicts both energy and forces utilizing E(3)-equivariant convolutions over geometric tensors. Evaluated on the MD17 data set its accuracy exceeds those of existing models while needing up to three orders of magnitude less training data. Due...
5 LESS POWERFUL BUT STILL INTERESTING GNNS 接下来,我们研究一下不满足 Theorem 3 (单射)条件的 GNNs,包括 GCN,GraphSAGE。再从两个方面开展关于聚合函数 Eq4.1 的消融实验 用1-layer 的感知机而不采用 MLPs(单纯邻域节点线性求和作为聚合策略是否可行) ...
with them. In recent years, systems based on variants of graph neural networks such as graph convolutional network (GCN), graph attention network (GAT), gated graph neural network (GGNN) have demonstrated ground-breaking performance on many tasks mentioned above. In this survey, we provide a ...
2. Neural Networks as Relational Graphs To explore the graph structure of neural networks, we first introduce the concept of our relational graph representation and its instantiations. We demonstrate how our representation can capture diverse neural network architectures under a unified framework. Using ...