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 331–340 of 982 papers

TitleStatusHype
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation LearningCode0
Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition—0
OCTAL: Graph Representation Learning for LTL Model Checking—0
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field—0
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based SimilarityCode1
Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic NetworksCode2
Biomedical Knowledge Graph Embeddings with Negative StatementsCode0
Local Structure-aware Graph Contrastive Representation Learning—0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
SimTeG: A Frustratingly Simple Approach Improves Textual Graph LearningCode1
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Benchmark Results

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