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 801–810 of 1192 papers

TitleStatusHype
Multi-view Graph Convolutional Networks with Differentiable Node Selection—0
Multi-View Graph Embedding Using Randomized Shortest Paths—0
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis—0
Toward Understanding The Effect of Loss Function on The Performance of Knowledge Graph Embedding—0
Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis—0
Toward Understanding The Effect of Loss Function on The Performance of Knowledge Graph Embedding—0
NASGEM: Neural Architecture Search via Graph Embedding Method—0
Negative Sampling in Knowledge Graph Representation Learning: A Review—0
TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models—0
Neighbor2vec: an efficient and effective method for Graph Embedding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified