「标题」:p-Laplacian Based Graph Neural Networks「作者」:Guoji Fu, Peilin Zhao, Yatao Bian「链接」:proceedings.mlr.press/v 内容简介 由于能够利用节点特征和拓扑信息,图神经网络 (GNNs) 在图上的半监督节点分类方面表现出卓越的性能。 然而,大多数 GNNs 隐含地假设图中节点及其邻居的标签是相同或一致的,...
根据卷积层叠的不同方法,基于空间的GCN可以进一步分为两类:recurrent-based和composition-based的空间GCN。recurrent-based的方法使用相同的图卷积层来更新隐藏表示,composition-based的方法使用不同的图卷积层来更新隐藏表示。下图说明了这种差异。 1.3 Comparison Between Spectral and Spatial Models 作为最早的图卷积网络,...
Inductive Matrix Completion Based on Graph Neural Networks 参考文献 Inductive Matrix Completion Based on Graph Neural Networks - ICLR 2020 〇、相关工作 1、Graph
在这项工作中,我们介绍了一种基于词典的图神经网络lexicon-based graph neural network(LGN),它实现了中文NER作为节点分类任务。该模型打破了RNN的串行化处理结构,通过仔细的连接,字符和单词之间的交互效果更好。词汇知识将相关字符连接起来,以捕获本地成分。同时,设计了一个全局中继节点来捕获远程依赖性和高级特性。LG...
& Kou, G. Stock Movement Prediction Based on Bi-Typed Hybrid-Relational Market Knowledge Graph Via Dual Attention Networks. IEEE Transactions on Knowledge and Data Engineering (2022).REFERENCES编辑:王菁 数据派研究部介绍 数据派研究部成立...
Zhang, Y., Yao, Q., Yue, L.et al.Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network.Nat Comput Sci3, 1023–1033 (2023). https://doi.org/10.1038/s43588-023-00558-4 Download citation ...
推荐系统的发展可分为三个阶段:shallow models -> neural network-based models -> GNN models。其中: shallow models 最早的推荐系统是利用协同过滤(Collaborative Filtering,CF)来计算user和item之间的相似度。后续在此基础上又提出了matrix factorization(MF)、factorization machine等方法。
Graph-based Neural Networks This page is to summarize important materials about graph-based neural networks and relational networks. If I miss some recent works or anyone wants to recommend other references, please let me know.Background(You can find many materials for deep neural networks in ...
In response to the data characteristics of subgrade engineering slope protection design schemes, this study proposed an intelligent decision-making technology based on graph neural networks (GNNs). Firstly, by reviewing a large number of well-established design case...
Temporal Augmented Graph Neural Networks for Session-Based Recommendations https://dl.acm.org/doi/abs/10.1145/3404835.3463112 这篇是做session推荐的短文。motivation是序列推荐在实时性上的几个困难, 实时载入很麻烦。将整个历史会话加载到内存中并一次训练模型会变得很昂贵,同时session还可以不断增长。