Graph 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. However, the existing methods assume that graph nodes and edges are of the ...
Analyzing Heterogeneous Networks With Missing Attributes by Unsupervised Contrastive Learning 2024, IEEE Transactions on Neural Networks and Learning Systems Graph representation learning in bioinformatics: Trends, methods and applications 2022, Briefings in Bioinformatics HDMI: High-order deep multiplex infomax ...
To the best of our knowledge, this is the first time ESNs are studied within the framework of multiplex networks. However, it is worth citing a loosely related paper by Zhang et al.47, where the authors set the basis for a graph-theoretical analysis of RNNs. Here, we represent the ...
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嵌入 为了区分相邻节点的影响,GAT[27]被提出作为一种关注的消息传递机制来...
multiplex graph neural networks (GNNs) directly initialize node attributes as a feature vector for node representation learning, but they cannot fully capture the semantics of the nodes' associated texts. To bridge these gaps, we propose METAG, a new framework for learning Multiplex rEpresentations ...
Although some heuristic algorithms have been extended into multiplex networks, little work on neural models have been done so far. In this paper, we propose a graph convolutional fusion model (GCFM) for community detection in multiplex networks, which takes account of both intra-layer structural ...
Multiplex networks convey more valuable information than single-layer networks; thus, performing the community detection task involving these networks has become a subject of extensive research on the exploration of latent community structures. The non-negative matrix factorization (NMF) algorithm has prove...
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...
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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...