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 651–660 of 982 papers

TitleStatusHype
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning—0
Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs—0
Sparse-Dyn: Sparse Dynamic Graph Multi-representation Learning via Event-based Sparse Temporal Attention Network—0
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning—0
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs—0
EMP: Effective Multidimensional Persistence for Graph Representation Learning—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning—0
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning—0
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Benchmark Results

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