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 641–650 of 982 papers

TitleStatusHype
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
Dynamic Graph Representation Learning for Passenger Behavior Prediction—0
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability—0
Dynamic Graph Representation Learning with Neural Networks: A Survey—0
Dynamic Spiking Framework for Graph Neural Networks—0
DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation—0
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction—0
EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test—0
Edge but not Least: Cross-View Graph Pooling—0
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence—0
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Benchmark Results

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