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 841–850 of 982 papers

TitleStatusHype
Graph Contrastive Learning for Connectome ClassificationCode0
Biomedical Knowledge Graph Embeddings with Negative StatementsCode0
Cycle Invariant Positional Encoding for Graph Representation LearningCode0
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation LearningCode0
CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and PredictionCode0
Unbiased and Efficient Self-Supervised Incremental Contrastive LearningCode0
Cross-View Graph Consistency Learning for Invariant Graph RepresentationsCode0
Graph Communal Contrastive LearningCode0
Learning multi-resolution representations of research patterns in bibliographic networksCode0
Variational Graph Contrastive LearningCode0
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Benchmark Results

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