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
Is Distance Matrix Enough for Geometric Deep Learning?Code1
Jointly Learnable Data Augmentations for Self-Supervised GNNsCode1
Data Augmentation on Graphs: A Technical SurveyCode1
Multi-hop Attention Graph Neural NetworkCode1
Learning-Based Link Anomaly Detection in Continuous-Time Dynamic GraphsCode1
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)Code1
Learning Semantic-Specific Graph Representation for Multi-Label Image RecognitionCode1
LMSOC: An Approach for Socially Sensitive PretrainingCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
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Benchmark Results

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