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 901–910 of 982 papers

TitleStatusHype
Geometric Scattering Attention NetworksCode0
Robust Causal Graph Representation Learning against Confounding EffectsCode0
Robust Graph Representation Learning via Neural SparsificationCode0
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node ClassificationCode0
MM-GATBT: Enriching Multimodal Representation Using Graph Attention NetworkCode0
MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature DistributionCode0
Augment to Interpret: Unsupervised and Inherently Interpretable Graph EmbeddingsCode0
Model-Aware Contrastive Learning: Towards Escaping the DilemmasCode0
An Empirical Study of Retrieval-enhanced Graph Neural NetworksCode0
GEFL: Extended Filtration Learning for Graph ClassificationCode0
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Benchmark Results

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