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 131–140 of 982 papers

TitleStatusHype
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Multi-hop Attention Graph Neural NetworkCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Does Graph Distillation See Like Vision Dataset Counterpart?Code1
CCGL: Contrastive Cascade Graph LearningCode1
A Representation Learning Framework for Property GraphsCode1
DropMessage: Unifying Random Dropping for Graph Neural NetworksCode1
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