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
Deep Representation Learning For Multimodal Brain Networks—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
Deep Prompt Tuning for Graph Transformers—0
A Unified Graph Selective Prompt Learning for Graph Neural Networks—0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Deep Multi-attribute Graph Representation Learning on Protein Structures—0
Deep Modularity Networks with Diversity--Preserving Regularization—0
Spectral-Aware Augmentation for Enhanced Graph Representation Learning—0
Deep Learning on Graphs for Natural Language Processing—0
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Benchmark Results

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