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 111–120 of 982 papers

TitleStatusHype
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
GraphSAINT: Graph Sampling Based Inductive Learning MethodCode1
DropMessage: Unifying Random Dropping for Graph Neural NetworksCode1
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
Boosting Graph Structure Learning with Dummy NodesCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
Large-Scale Representation Learning on Graphs via BootstrappingCode1
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on EchocardiogramsCode1
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Benchmark Results

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