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 791–800 of 982 papers

TitleStatusHype
Distill2Vec: Dynamic Graph Representation Learning with Knowledge DistillationCode0
Calibrating and Improving Graph Contrastive LearningCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security PoliciesCode0
Graph Representation Learning: A SurveyCode0
Transformers are efficient hierarchical chemical graph learnersCode0
A knowledge graph representation learning approach to predict novel kinase-substrate interactionsCode0
Product Manifold Representations for Learning on Biological PathwaysCode0
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic TransformsCode0
Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural NetworksCode0
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Benchmark Results

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