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 121–130 of 982 papers

TitleStatusHype
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor IsomorphismCode1
Hierarchical Graph Representation Learning with Differentiable PoolingCode1
Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation LearningCode1
How Powerful are Graph Neural Networks?Code1
An Open Challenge for Inductive Link Prediction on Knowledge GraphsCode1
Implicit Graphon Neural RepresentationCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
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Benchmark Results

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