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 241–250 of 982 papers

TitleStatusHype
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
FMGNN: Fused Manifold Graph Neural Network—0
Dual Graph Representation Learning—0
Dual Space Graph Contrastive Learning—0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning—0
Domain Adaptive Graph Classification—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers—0
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Benchmark Results

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