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 211–220 of 982 papers

TitleStatusHype
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
GraphGT: Machine Learning Datasets for Graph Generation and TransformationCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Reward Propagation Using Graph Convolutional NetworksCode1
Graph Neural Networks with Adaptive ResidualCode1
Scaling Up Dynamic Graph Representation Learning via Spiking Neural NetworksCode1
Graph Mixture Density NetworksCode1
Self-supervised Graph-level Representation Learning with Local and Global StructureCode1
Learning Long Range Dependencies on Graphs via Random WalksCode1
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text ClassificationCode1
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Benchmark Results

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