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 941–950 of 982 papers

TitleStatusHype
Siamese Attribute-missing Graph Auto-encoder—0
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning—0
SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning—0
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration—0
Simple yet Effective Gradient-Free Graph Convolutional Networks—0
Simple yet Effective Graph Distillation via Clustering—0
SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs—0
Sparse Decomposition of Graph Neural Networks—0
Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference—0
Spatio-Temporal Graph Representation Learning for Fraudster Group Detection—0
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Benchmark Results

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