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
Neural Spacetimes for DAG Representation Learning—0
Disentangled Generative Graph Representation Learning—0
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural NetworksCode0
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small ModelsCode0
Dynamic Graph Representation Learning for Passenger Behavior Prediction—0
CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning—0
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation—0
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning—0
Knowledge Probing for Graph Representation Learning—0
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
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Benchmark Results

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