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 81–90 of 982 papers

TitleStatusHype
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural NetworksCode1
Is Distance Matrix Enough for Geometric Deep Learning?Code1
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal KrigingCode1
LazyGNN: Large-Scale Graph Neural Networks via Lazy PropagationCode1
Simultaneous Linear Multi-view Attributed Graph Representation Learning and ClusteringCode1
Simplifying Subgraph Representation Learning for Scalable Link PredictionCode1
Logical Message Passing Networks with One-hop Inference on Atomic FormulasCode1
A Generalization of ViT/MLP-Mixer to GraphsCode1
Data Augmentation on Graphs: A Technical SurveyCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
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Benchmark Results

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