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 676–700 of 982 papers

TitleStatusHype
GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements—0
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation—0
Revisiting the role of heterophily in graph representation learning: An edge classification perspective—0
Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?—0
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation—0
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs—0
Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security PoliciesCode0
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning—0
End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning—0
All-optical graph representation learning using integrated diffractive photonic computing units—0
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph RepresentationsCode0
A Survey on Graph Representation Learning Methods—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Explainability in Graph Neural Networks: An Experimental Survey—0
Few-Shot Learning on Graphs—0
Graph Representation Learning with Individualization and Refinement—0
Graph Representation Learning for Popularity Prediction Problem: A Survey—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
Graph Representation Learning Beyond Node and HomophilyCode0
Understanding microbiome dynamics via interpretable graph representation learningCode0
Distribution Preserving Graph Representation Learning—0
Message passing all the way up—0
Interactive Visual Pattern Search on Graph Data via Graph Representation Learning—0
Adversarial Graph Contrastive Learning with Information RegularizationCode0
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Benchmark Results

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