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 181–190 of 982 papers

TitleStatusHype
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning—0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Fair Graph Representation Learning via Sensitive Attribute DisentanglementCode0
PAC-Bayesian Generalization Bounds for Knowledge Graph Representation LearningCode1
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships—0
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers—0
Temporal Graph ODEs for Irregularly-Sampled Time SeriesCode1
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning—0
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature AttacksCode0
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Benchmark Results

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