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 401–410 of 982 papers

TitleStatusHype
Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation LearningCode0
GraphMatcher: A Graph Representation Learning Approach for Ontology MatchingCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Robust Graph Representation Learning for Local Corruption RecoveryCode0
On the Initialization of Graph Neural NetworksCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance ModelCode0
Is Performance of Scholars Correlated to Their Research Collaboration Patterns?Code0
Enhancing Fairness in Unsupervised Graph Anomaly Detection through DisentanglementCode0
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Benchmark Results

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