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 511–520 of 982 papers

TitleStatusHype
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text ClassificationCode1
Machine Learning Partners in Criminal Networks—0
Temporal knowledge graph representation learning with local and global evolutionsCode0
Structure-Preserving Graph Representation LearningCode1
A Class-Aware Representation Refinement Framework for Graph Classification—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
A Survey on Temporal Graph Representation Learning and Generative Modeling—0
Relational Self-Supervised Learning on GraphsCode1
Robust Causal Graph Representation Learning against Confounding EffectsCode0
Modeling Two-Way Selection Preference for Person-Job FitCode1
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Benchmark Results

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