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 ...
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...
Recently, graph representation learning frameworks have made great efforts toward dynamic graph learning. Although dynamic graph methods have achieved impressive results, they require labeled data for model training. The contrastive learning does not require human annotation to complete model training and ...
6 dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning207 link:https://scholar.google.com.hk/scholar_url?url=https://arxiv.org/pdf/1809.02657&hl=zh-TW&sa=X&ei=bSGfYviOJOOEywThnbSYCQ&scisig=AAGBfm0bzwUuDvjnCXStu1Abuajctfd1xw&oi=scholarr DTDG Abstract ...
Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualization. Previous methods on graph representation learning mainly focus on static graphs, however, many real-world graphs are d...
Graph representation learning is an emerging task for effectively embedding graph-structured data with learned features. Among them, Subgraph-based GRL (SG... YuZihao,LiaoNingyi,LuoSiqiang - 《Proceedings of the Vldb Endowment》 被引量: 0发表: 2024年 Temporal resonant graph network for representat...
Graph representation learning techniques can be broadly divided into two categories: (i) static graph embedding, which represents each node in the graph with a single vector; and (ii) dynamic graph embedding, which considers multiple snapshots of a graph and obtains a time series of vectors for...
22. TemporalGAT: Attention-Based Dynamic Graph Representation Learning 作者:Ahmed Fathy and Kan Li(Beijing Institute of Technology) 发表时间:2020 发表于:PAKDD 2020 标签:DTDG,图神经网络 概述:目前的方法使用了时态约束权重(temporal regularized weights)来使节点在相邻时态状态的变化是平滑的,但是这种约束...
链接:Dynamic Graph Representation Learning via Self-Attention Networks 来自:ICML workshop 19, WSDM 19 学习图节点的表示是一项基本的任务,具有广泛的应用,例如链接预测,节点分类和图形可视化。之前的方法主要关注静态图,但是,许多现实世界中的图是动态的,并且会随着时间的推移而变化。在本文中,本文提出了动态自我注...