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 761–770 of 982 papers

TitleStatusHype
Asymmetric Graph Representation Learning—0
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs—0
Cycle Representation Learning for Inductive Relation PredictionCode0
Revisiting SVD to generate powerful Node Embeddings for Recommendation Systems—0
Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision LevelsCode0
Graph Representation Learning for Spatial Image Steganalysis—0
Reconstruction for Powerful Graph Representations—0
GLASS: GNN with Labeling Tricks for Subgraph Representation Learning—0
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms—0
Towards Feature Overcorrelation in Deeper Graph Neural Networks—0
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Benchmark Results

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