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 441–450 of 982 papers

TitleStatusHype
Discriminative Graph Autoencoder—0
Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning—0
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis—0
Graph Representation Learning with Individualization and Refinement—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning—0
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
Graph Self-Contrast Representation Learning—0
Few-Shot Learning on Graphs—0
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Benchmark Results

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