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 641–650 of 982 papers

TitleStatusHype
Revisiting Embeddings for Graph Neural Networks—0
Cell Attention NetworksCode0
Machine Learning Partners in Criminal Networks—0
Temporal knowledge graph representation learning with local and global evolutionsCode0
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
Robust Causal Graph Representation Learning against Confounding EffectsCode0
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?Code0
Representation Learning on Graphs to Identifying Circular Trading in Goods and Services Tax—0
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Benchmark Results

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