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 481–490 of 982 papers

TitleStatusHype
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability—0
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning—0
Dynamic Graph Representation Learning for Passenger Behavior Prediction—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning—0
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning—0
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions—0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
Dual Space Graph Contrastive Learning—0
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning—0
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Benchmark Results

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