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

Showing 951960 of 982 papers

TitleStatusHype
Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation LearningCode0
Enhancing Fairness in Unsupervised Graph Anomaly Detection through DisentanglementCode0
Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative SamplingCode0
A Deep Latent Space Model for Graph Representation LearningCode0
NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human ConnectomesCode0
ENGAGE: Explanation Guided Data Augmentation for Graph Representation LearningCode0
Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision LevelsCode0
Topological Pooling on GraphsCode0
Unsupervised Deep Manifold Attributed Graph EmbeddingCode0
Adaptive Sampling Towards Fast Graph Representation LearningCode0
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Benchmark Results

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