Dynamic Graph: Learning Instance-aware Connectivity for Neural NetworksKun YuanQuanquan LiDapeng ChenAojun ZhouJunjie Yan
Awesome-DynamicGraphLearning Awesome papers (codes) about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs) and their applications (i.e. Recommender Systems). Survey Papers 2025 Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding (WSDM...
Learning with Continuous-Time Dynamic Graphs The Benchmark Problem Spatio-temopral graph benchmark D-TDG benchmark C-TDG benchmark Abstract 深度图网络(DGNs)研究领域蓬勃发展,但仍有一些尚未解决的重要挑战尚未解决。具体来说,人们迫切希望使DGNs适应现实世界的互联实体系统上的预测任务,这些系统会随着时间的...
Awesome papers (codes) about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs) and their applications (i.e. recommendation, knowledge graph completion). GitHub Link:https://github.com/SpaceLearner/Awesome-DynamicGraphLearning Survey Representation Learning for ...
5 Dynamic Graph Representation Learning Via Self-Attention Networks link:https://arxiv.org/abs/1812.09430 Abstract 提出了在动态图上使用自注意力 Conclusion 本文提出了使用自注意力的网络结构用于在动态图学习节点表示。具体地说,DySAT使用(1)结构邻居和(2)历史节点表示上的自我注意来计算动态节点表示,虽然实验...
Dynamic Graph CNN for Learning on Point Clouds 图为使用提出的神经网络对点云进行分割的结果。底部为神经网络架构示意图。顶部为在网络的不同层上生成的特征空间的结构,红点到其他所有点的空间距离可视化(从左到右是输入和第1-3层的结果);最右边的图显示了分割的结果。尽管他们在原始输入空间上由很长的距离,...
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features,程序员大本营,技术文章内容聚合第一站。
摘要《Dynamic Graph CNN for Learning on Point Clouds》论文作者基于PointNet和PointNet++做了改进,提出了一个针对点云(Point Clouds)数据进行分类和分割的网络——DGCNN。 PointNet独立处理点云数据中的每个点来实现排列不变性(Permutation Invariance),忽略了点 论文笔记:DGCNN(EdgeConv) Dynamic Graph CNN for Learn...
This repository is built for the paperTowards Better Dynamic Graph Learning: New Architecture and Unified Library. 🔔 If you have any questions or suggestions, please feel free to let us know. You can directly emailLe Yuusing the email addressyule@buaa.edu.cnor post an issue on this reposit...
在GraphPage中使用不同的聚合器进行实验,即GCN、平均池、最大池和LSTM,以报告每个数据集中性能最好的聚合器的性能。为了与GAT进行公平比较,GAT最初只对节点分类进行实验,论文在GraphSAGE中实现了一个图形注意层作为额外的聚合器,用GraphSAGE+GAT表示。本文还将GCN和GAT训练为自动编码器,用于沿着(Modeling polypharmacy ...