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 711–720 of 982 papers

TitleStatusHype
Graph Trend Filtering Networks for RecommendationsCode1
Localized Graph Collaborative Filtering—0
Self-supervised Consensus Representation Learning for Attributed GraphCode0
DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation—0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Graph Neural Networks With Lifting-based Adaptive Graph Wavelets—0
JCapsR: 一种联合胶囊神经网络的藏语知识图谱表示学习模型(JCapsR: A Joint Capsule Neural Network for Tibetan Knowledge Graph Representation Learning)—0
CCGL: Contrastive Cascade Graph LearningCode1
Local2Global: Scaling global representation learning on graphs via local trainingCode0
Graph Representation Learning on Tissue-Specific Multi-Omics—0
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Benchmark Results

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