SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

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Showing 611–620 of 982 papers

TitleStatusHype
Deep Feature Learning for Graphs—0
Deep Graph Generators: A Survey—0
Deep Learning on Graphs for Natural Language Processing—0
Deep Modularity Networks with Diversity--Preserving Regularization—0
Deep Multi-attribute Graph Representation Learning on Protein Structures—0
Deep Prompt Tuning for Graph Transformers—0
Deep Representation Learning For Multimodal Brain Networks—0
DeepTrax: Embedding Graphs of Financial Transactions—0
Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networks—0
Detection of Fake Users in SMPs Using NLP and Graph Embeddings—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified