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 231–240 of 982 papers

TitleStatusHype
Position: Topological Deep Learning is the New Frontier for Relational Learning—0
Graph Mamba: Towards Learning on Graphs with State Space ModelsCode0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation LearningCode1
Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient MatchingCode0
On provable privacy vulnerabilities of graph representations—0
EXGC: Bridging Efficiency and Explainability in Graph CondensationCode0
Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques—0
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Scalable and Efficient Temporal Graph Representation Learning via Forward Recent SamplingCode0
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Benchmark Results

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