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

Showing 181190 of 982 papers

TitleStatusHype
Logical Message Passing Networks with One-hop Inference on Atomic FormulasCode1
M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender SystemsCode1
Data Augmentation on Graphs: A Technical SurveyCode1
Fast Graph Representation Learning with PyTorch GeometricCode1
FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation LearningCode1
Metric Based Few-Shot Graph ClassificationCode1
Modeling Two-Way Selection Preference for Person-Job FitCode1
Molecular Representation Learning via Heterogeneous Motif Graph Neural NetworksCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
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Benchmark Results

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