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 661–670 of 982 papers

TitleStatusHype
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks—0
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning—0
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach—0
Equivariant Quantum Graph Circuits—0
ETA Prediction with Graph Neural Networks in Google Maps—0
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning—0
Everything is Connected: Graph Neural Networks—0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
Explainability in Graph Neural Networks: An Experimental Survey—0
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs—0
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Benchmark Results

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