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 481–490 of 982 papers

TitleStatusHype
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
Privacy-preserving design of graph neural networks with applications to vertical federated learning—0
A Causal Disentangled Multi-Granularity Graph Classification Method—0
Knowledge-Induced Medicine Prescribing Network for Medication Recommendation—0
Spectral-Aware Augmentation for Enhanced Graph Representation Learning—0
Graph AI in Medicine—0
Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation LearningCode0
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning—0
Self-supervision meets kernel graph neural models: From architecture to augmentations—0
Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural NetworksCode0
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Benchmark Results

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