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 501–510 of 982 papers

TitleStatusHype
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge GraphsCode0
Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning—0
Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference—0
Graph Self-Contrast Representation Learning—0
RDGSL: Dynamic Graph Representation Learning with Structure LearningCode0
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative samplingCode0
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability—0
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs—0
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation LearningCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
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Benchmark Results

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