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 381–390 of 982 papers

TitleStatusHype
Decoupling feature propagation from the design of graph auto-encoders—0
Generalized Laplacian Positional Encoding for Graph Representation Learning—0
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck—0
A General-Purpose Transferable Predictor for Neural Architecture Search—0
A Benchmark on Directed Graph Representation Learning in Hardware Designs—0
Graph Contrastive Learning with Generative Adversarial Network—0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
Graph Representation Learning on Tissue-Specific Multi-Omics—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs—0
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Benchmark Results

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