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 451–460 of 982 papers

TitleStatusHype
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing—0
Graph Transformers without Positional Encodings—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—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
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules—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
Few-Shot Learning on Graphs—0
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Benchmark Results

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