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 731–740 of 982 papers

TitleStatusHype
Graph Representation Learning via Contrasting Cluster Assignments—0
Equivariant Quantum Graph Circuits—0
A Self-supervised Mixed-curvature Graph Neural Network—0
Siamese Attribute-missing Graph Auto-encoder—0
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training—0
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations—0
Controversy Detection: a Text and Graph Neural Network Based Approach—0
Consensus Graph Representation Learning for Better Grounded Image Captioning—0
Do Transformers Really Perform Badly for Graph Representation?Code0
Hierarchical Prototype Networks for Continual Graph Representation Learning—0
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Benchmark Results

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