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 571–580 of 982 papers

TitleStatusHype
OCTAL: Graph Representation Learning for LTL Model Checking—0
OFFER: A Motif Dimensional Framework for Network Representation Learning—0
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning—0
OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning—0
Hyperdimensional Representation Learning for Node Classification and Link Prediction—0
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks—0
On Node Features for Graph Neural Networks—0
On provable privacy vulnerabilities of graph representations—0
On the combination of graph data for assessing thin-file borrowers' creditworthiness—0
On the Interpretability and Evaluation of Graph Representation Learning—0
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Benchmark Results

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