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

Showing 801810 of 982 papers

TitleStatusHype
PROXI: Challenging the GNNs for Link PredictionCode0
About Graph Degeneracy, Representation Learning and ScalabilityCode0
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural NetworksCode0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph CondensationCode0
DINE: Dimensional Interpretability of Node EmbeddingsCode0
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter TuningCode0
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Graph Pooling via Coarsened Graph InfomaxCode0
Investigating Similarities Across Decentralized Financial (DeFi) ServicesCode0
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Benchmark Results

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