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 726–750 of 982 papers

TitleStatusHype
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
Learning to Model the Relationship Between Brain Structural and Functional ConnectomesCode0
Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast—0
Knowledge-enhanced Session-based Recommendation with Temporal Transformer—0
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
Graph Representation Learning via Contrasting Cluster Assignments—0
Equivariant Quantum Graph Circuits—0
A Self-supervised Mixed-curvature Graph Neural Network—0
Siamese Attribute-missing Graph Auto-encoder—0
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training—0
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations—0
Controversy Detection: a Text and Graph Neural Network Based Approach—0
Consensus Graph Representation Learning for Better Grounded Image Captioning—0
Do Transformers Really Perform Badly for Graph Representation?Code0
Hierarchical Prototype Networks for Continual Graph Representation Learning—0
On the combination of graph data for assessing thin-file borrowers' creditworthiness—0
Multi-fidelity Stability for Graph Representation Learning—0
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming—0
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability—0
Pre-training Graph Neural Network for Cross Domain Recommendation—0
Structure and Features Fusion with Evidential Graph Convolutional Neural Network for Node Classification—0
CN-Motifs Perceptive Graph Neural Networks—0
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
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Benchmark Results

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