Advanced Graph Neural Networks
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Scope of this subject
This cluster of papers focuses on the development, applications, and techniques related to Graph Neural Networks (GNNs) and their variants. It covers topics such as knowledge graph embedding, representation learning, network embedding, deep learning, graph convolutional networks, heterogeneous networks, relational data modeling, signal processing on graphs, and semi-supervised learning.
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Research vocabulary
link prediction; node classification; graph representation learning; graph embedding; knowledge graph embedding; knowledge graph completion; node embeddings; graph classification; heterogeneous graphs; node representation learning; over-smoothing; graph signal processing; graph learning; temporal knowledge graphs; graph contrastive learning; knowledge graph reasoning; entity embeddings; graph structure learning; attributed graphs; graph transformer; entity alignment; graph augmentation; heterogeneous graph neural networks; graph-structured data; network representation learning
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Local answer status
No owner-reviewed answer has been published for this exact subject page. This is a gap in this collection; it does not establish that the answer is unavailable elsewhere.
Numeric address and provenance
Morton address: 36183004602860635 · morton4x16-v1
Coordinates: (3, 17, 1702, 11273). The address interleaves categorical identifier bits. Nearby addresses are not guaranteed to describe semantically similar subjects.
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