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 431–440 of 982 papers

TitleStatusHype
Graph Representation Learning for Popularity Prediction Problem: A Survey—0
Diffusion Model Agnostic Social Influence Maximization in Hyperbolic Space—0
Graph Representation Learning for Spatial Image Steganalysis—0
Graph representation learning for street networks—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
Graph Representation Learning on Tissue-Specific Multi-Omics—0
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease—0
Graph Representation Learning Towards Patents Network Analysis—0
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
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Benchmark Results

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