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 781–790 of 982 papers

TitleStatusHype
Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning—0
Graph-based Incident Aggregation for Large-Scale Online Service SystemsCode0
ETA Prediction with Graph Neural Networks in Google Maps—0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Self-supervised Consensus Representation Learning for Attributed GraphCode0
Localized Graph Collaborative Filtering—0
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
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Benchmark Results

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