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 111–120 of 982 papers

TitleStatusHype
Scaling Up Dynamic Graph Representation Learning via Spiking Neural NetworksCode1
Motif-based Graph Representation Learning with Application to Chemical MoleculesCode1
Generative Subgraph Contrast for Self-Supervised Graph Representation LearningCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
A Representation Learning Framework for Property GraphsCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Boosting Graph Structure Learning with Dummy NodesCode1
Taxonomy of Benchmarks in Graph Representation LearningCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
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Benchmark Results

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