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 461–470 of 982 papers

TitleStatusHype
EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test—0
FairMILE: Towards an Efficient Framework for Fair Graph Representation LearningCode0
Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning—0
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification—0
Holder Recommendations using Graph Representation Learning & Link Prediction—0
MGTCOM: Community Detection in Multimodal GraphsCode0
Graph representation learning for street networks—0
Hyperbolic Graph Representation Learning: A Tutorial—0
Implicit Graphon Neural RepresentationCode1
Application of Graph Neural Networks and graph descriptors for graph classification—0
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Benchmark Results

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