The algorithm consists of two modules: self-sensing neighborhood aggregation algorithm and dynamic graph structure learning algorithm based on RNN. GraphSense can make each node discover more valuable neighbors through the self-aware neighborhood aggregation algorithm in each epoch. The algorithm employs ...
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 ...
3. Dynamic Graph Learning 3.1. Static Graph Learning Prior to proposing the dynamic structure learning of a graph, we briefly revisit the basic notions of static graph learning [11]. Using the Laplacian quadratic form 𝑡𝑟(𝐗𝑇ℒ(𝐖)𝐗)trXTL(W)X as a smoothness regularizer of th...
Figure 1. Traditional clustering-based unsupervised representation learning. This work presents the dynamic graph clustering learning (DGCL) model for unsupervised classification of diabetic retinopathy. The DGCL model incorporates three key modules: the multi-structural feature fusion (MFF) module, the ...
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...
Graph is a data structure that represents the node information and the node relationship, which is ubiquitous in practice. 传统GCN 给定 ,通过两层的GCN可以得到表示输出 : 该论文的目标是全面挖掘节点之间的隐式特征信息,并通过图神经网络构建信息更丰富、更鲁棒的图节点嵌入。首先根据初始特征图的邻接矩阵 ...
(2020). Graph structure learning for robust graph neural networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 66–74. Karim, R., Zhao, H., Wildes, R. P., & Siam, M. (2023). MED-VT: Multiscale encoder-decoder video ...
标签:Inductive Graph Embedding 概述:针对以往transductive的方式(不能表示unseen nodes)的方法作了改进,提出了一种inductive的方式改进这个问题,该方法学习聚合函数,而不是某个节点的向量表示。 链接:https://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf ...
时空网络(拓扑是静态的,只有节点或边缘特征改变[14]的图(structure RNN))不在本研究的范围内,时空图神经网络[14],[15](Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting)也不在本研究的范围内。 与gnn和其他表示学习模型一样,dgnn是通用的,可以应用于各种任务。使...
We propose to compose dynamic tree structures that place the objects in an image into a visual context, helping visual reasoning tasks such as scene graph generation and visual Q&A. Our visual context tree model, dubbed VCTree, has two key advantages over existing structured object representations...