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 591–600 of 982 papers

TitleStatusHype
Perturbation Ontology based Graph Attention Networks—0
Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations—0
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation—0
Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning—0
Polyp-artifact relationship analysis using graph inductive learned representations—0
Position: Topological Deep Learning is the New Frontier for Relational Learning—0
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields—0
Tackling the Local Bias in Federated Graph Learning—0
A Graph Feature Auto-Encoder for the Prediction of Unobserved Node Features on Biological Networks—0
A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets—0
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Benchmark Results

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