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 291–300 of 982 papers

TitleStatusHype
Privacy-preserving design of graph neural networks with applications to vertical federated learning—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
A Causal Disentangled Multi-Granularity Graph Classification Method—0
Knowledge-Induced Medicine Prescribing Network for Medication Recommendation—0
UniMAP: Universal SMILES-Graph Representation LearningCode1
Graph AI in Medicine—0
Spectral-Aware Augmentation for Enhanced Graph Representation Learning—0
Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation LearningCode0
Self-supervision meets kernel graph neural models: From architecture to augmentations—0
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Benchmark Results

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