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 131–140 of 982 papers

TitleStatusHype
Multi-modal Graph Learning for Disease PredictionCode1
An Open Challenge for Inductive Link Prediction on Knowledge GraphsCode1
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation LearningCode1
Sign and Basis Invariant Networks for Spectral Graph Representation LearningCode1
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data AugmentationCode1
Molecular Representation Learning via Heterogeneous Motif Graph Neural NetworksCode1
When Do Flat Minima Optimizers Work?Code1
Graph Representation Learning via Aggregation EnhancementCode1
GRPE: Relative Positional Encoding for Graph TransformerCode1
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Benchmark Results

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