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 591–600 of 982 papers

TitleStatusHype
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance ModelCode0
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective—0
Everything is Connected: Graph Neural Networks—0
A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge—0
Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning—0
WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation LearningCode0
Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations—0
Graph Learning with Localized Neighborhood Fairness—0
Robust Graph Representation Learning via Predictive Coding—0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
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Benchmark Results

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