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 571–580 of 982 papers

TitleStatusHype
Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework—0
Towards Improved Illicit Node Detection with Positive-Unlabelled LearningCode0
Prior Information based Decomposition and Reconstruction Learning for Micro-Expression Recognition—0
A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail—0
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks—0
Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations—0
A General-Purpose Transferable Predictor for Neural Architecture Search—0
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
Creating generalizable downstream graph models with random projections—0
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder IdentificationCode0
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Benchmark Results

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