The idea leads to a simple and efficient graph similarity, which we name Weisfeiler鈥揕eman similarity (WLS). In contrast to existing graph kernels, WLS is easy to implement with common deep learning frameworks. In graph classification experiments, transform-sum-cat significantly outperforms other ...
A graph similarity for deep learning Graph neural networks (GNNs) have been successful in learning representations from graphs. Many popular GNNs follow the pattern of aggregate-transform: they aggregate the neighbors' attributes and then transform the results of aggregatio... S Ok 被引量: 0发表...
graph diffusion graph sampling 1.2.1 Edge Addition/Dropping 即 保留原始节点顺序,对邻接矩阵种的元进行改写。 基于图稀疏性(graph sparsification)的图结构优化方法 [8、9],基于图结构整洁性(graph sanitation)的方法 [3],以及图采样(graph sampling)。
我们进一步将图数据增强的应用总结进两个代表性的data-centric的深度图学习的问题:(1)reliable graph learning:关注增强输入图的效用以及模型容量(通过图数据增强)(2)low-resource graph learning:目标是通过数据增强来增强带标签的训练数据范围。 1. introduction 本文的贡献如下: (1)本文是GraphDA(图数据增强)的第...
《Deep Learning on Graphs: A Survey》。 Zhang Z, Cui P, Zhu W. Deep learning on graphs: A survey[J]. IEEE Transactions on Knowledge and Data Engineering, 2020. 18年的一篇GNN综述,读完之后,感觉GCN那一部分对我帮助还不小,帮我理清了脉络,也可能是因为之前把《Graph Representation Learning》这本...
[WSDM 2021]Learning to Drop: Robust Graph Neural Network via Topological Denoising[Paper|Code] [WSDM 2021]Node Similarity Preserving Graph Convolutional Networks[Paper|Code] [IJCAI 2021]Understanding Structural Vulnerability in Graph Convolutional Networks[Paper|Code] ...
首先,使用图级表示技术(如Graph2Vec [144], FGSD[145])将数据库中的图编码到共享潜在空间中。然后,使用现成的异常值检测器来测量每个图的异常值。从本质上讲,这种方法需要对两个阶段的现有方法进行配对,但这两个阶段之间是相互分离的,因此,由于嵌入相似性不一定是为了异常检测而设计的,因此检测性能可能会很差...
Graph Transformer Architecture Source code for the paper "A Generalization of Transformer Networks to Graphs" byVijay Prakash DwivediandXavier Bresson, atAAAI'21 Workshop on Deep Learning on Graphs: Methods and Applications (DLG-AAAI'21).
Focusing on the problems, we propose a leader-follower flocking algorithm based on a novel reinforcement learning (RL) model. We construct a homogeneous graph neural network (GNN) based multi-agent deep deterministic policy gradient (herein HGNN-MADDPG) algorithm model for multi-agent flocking ...
本文提出一种基于graph-LSTM的方法来直接根据输入图的拓扑结构生成图布局结果。本文使用广度优先搜索(BFS)将图拓扑信息转换为一系列邻接矩阵,其中每个子向量对序列中每个节点及其相邻节点之间的连接信息进行编码。还提出了一种基于Prostats Statistics的损失函数,该函数对于图的平移,旋转和缩放基本上是不变的,以此评估学习...