Graph neural networkHeterogeneous graphMeta-pathsRolling bearingsRemaining useful lifeGraph neural networks (GNNs) can capture interdependencies between data with the structured data modeling ability, and have received much attention from industry professionals in remaining useful life (RUL) prediction tasks....
This would allow to further explore and analyze the route to chaos in input-driven neural models by exploiting the language of graph theory, which is an already established framework within the neuroscience field67. References 1. Barzel, B. & Barabási, A.-L. Universality in network dynamics....
In this work, we present an unsupervised network embedding method for attributed multiplex network called DMGI, inspired by Deep Graph Infomax (DGI) that maximizes the mutual information between local patches of a graph, and the global representation of the entire graph. Building on top of DGI,...
2019. Heterogeneous graph neural network. In KDD. 793–803. 相关工作 Graph Neural Networks(GNN,图神经网络): GNN的目标是学习每个节点的低维向量表示,这可以用于许多下游网络挖掘任务。 Kipf等[15]提出在图邻节点上执行卷积运算进行信息聚合 GraphSAGE[7]是一个归纳式GNN框架,它使用通用聚合函数来高效生成节点...
Multiplex network Graph neural networks Deep learning Access this article Log in via an institution Subscribe and save Springer+ Basic €32.70 /Month Get 10 units per month Download Article/Chapter or eBook 1 Unit = 1 Article or 1 Chapter Cancel anytime Subscribe now Buy Now Buy article ...
Wang X, Liu N, Han H, Shi C (2021) Self-supervised heterogeneous graph neural network with co-contrastive learning. KDD 1726–1736 Zou X, Zheng Q, Dong Y, Guan X, Kharlamov E, Lu J, Tang J (2021) TDGIA: Effective injection attacks on graph neural networks. KDD 2461–2471 Kipf TN...
GraphSAGE [50] is a graph neural network framework for inductive representation learning on large graphs. GraphSAGE is used to generate low-dimensional vector representations for nodes and is especially useful for graphs that have rich node attribute information. We use an unsupervised learning version...
概 符号说明 各种定义 Heterogeneous network Attributed network Attributed multiplex network 代码 __EOF__ 分类: Representation Learning 标签: emmm, empirical, GNN, graph, heterogeneous 馒头and花卷 粉丝- 95 关注- 1 会员号:2578(终身会员VIP) +加关注 0 0 « 上一篇: Invariant and Equivariant ...
Multiplex networks convey more valuable information than single-layer networks; thus, performing the community detection task involving these networks has
Temporal heterogeneous interaction graph embedding for next-item recommendation (PKDD'20) Link Inference via Heterogeneous Multi-view Graph Neural Networks (DASFAA 2020) Multi-View Collaborative Network Embedding (Arxiv, May 2020) Please let me know if your toolkit includes GATNE models or your paper...