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 661–670 of 982 papers

TitleStatusHype
Pre-training Graph Neural Network for Cross Domain Recommendation—0
CN-Motifs Perceptive Graph Neural Networks—0
Implicit SVD for Graph Representation LearningCode1
Inferential SIR-GN: Scalable Graph Representation Learning—0
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data AugmentationsCode0
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices—0
Knowledge Graph Representation Learning using Ordinary Differential Equations—0
Geo-BERT Pre-training Model for Query Rewriting in POI Search—0
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule MiningCode0
Hierarchical Heterogeneous Graph Representation Learning for Short Text ClassificationCode1
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Benchmark Results

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