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 1176–1192 of 1192 papers

TitleStatusHype
TreeRNN: Topology-Preserving Deep GraphEmbedding and LearningCode0
TuckerDNCaching: high-quality negative sampling with tucker decompositionCode0
Twitter User Representation Using Weakly Supervised Graph EmbeddingCode0
Two Layer Walk: A Community-Aware Graph EmbeddingCode0
Two-view Graph Neural Networks for Knowledge Graph CompletionCode0
Unified Interpretation of Smoothing Methods for Negative Sampling Loss Functions in Knowledge Graph EmbeddingCode0
Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph EmbeddingCode0
Universal Knowledge Graph EmbeddingsCode0
Unsupervised Deep Manifold Attributed Graph EmbeddingCode0
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph ProximityCode0
Universal Graph Transformer Self-Attention NetworksCode0
Valid Conformal Prediction for Dynamic GNNsCode0
Visualizing DNA reaction trajectories with deep graph embedding approachesCode0
Wasserstein Graph Distance Based on L_1-Approximated Tree Edit Distance between Weisfeiler-Lehman SubtreesCode0
Watch Your Step: Learning Node Embeddings via Graph AttentionCode0
Which way? Direction-Aware Attributed Graph EmbeddingCode0
Whole-Graph Representation Learning For the Classification of Signed NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified