SOTAVerified

Graph Embedding

Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.

( Image credit: GAT )

Papers

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Showing 901–910 of 1192 papers

TitleStatusHype
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder IdentificationCode0
Unify Local and Global Information for Top-N RecommendationCode0
Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled GraphsCode0
AdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram ModelCode0
A flexible PageRank-based graph embedding framework closely related to spectral eigenvector embeddingsCode0
A Geometric Perspective for High-Dimensional Multiplex GraphsCode0
All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional NetworksCode0
An Adversarial Transfer Network for Knowledge Representation LearningCode0
An Attention-based Graph Neural Network for Heterogeneous Structural LearningCode0
An FEA surrogate model with Boundary Oriented Graph Embedding approachCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified