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 641–650 of 982 papers

TitleStatusHype
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast—0
Graph Representation Learning via Contrasting Cluster Assignments—0
A Self-supervised Mixed-curvature Graph Neural Network—0
Equivariant Quantum Graph Circuits—0
Siamese Attribute-missing Graph Auto-encoder—0
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations—0
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training—0
Controversy Detection: a Text and Graph Neural Network Based Approach—0
Consensus Graph Representation Learning for Better Grounded Image Captioning—0
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Benchmark Results

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