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
Class-Imbalanced Learning on Graphs: A SurveyCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Graph Representation Learning for Multi-Task Settings: a Meta-Learning ApproachCode1
GRPE: Relative Positional Encoding for Graph TransformerCode1
Graph Trend Filtering Networks for RecommendationsCode1
Boosting Graph Structure Learning with Dummy NodesCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
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Benchmark Results

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