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 41–50 of 982 papers

TitleStatusHype
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation LearningCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt LearningCode1
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
Motif-aware Riemannian Graph Neural Network with Generative-Contrastive LearningCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
PC-Conv: Unifying Homophily and Heterophily with Two-fold FilteringCode1
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution GeneralizationCode1
Relational Deep Learning: Graph Representation Learning on Relational DatabasesCode1
Recurrent Distance Filtering for Graph Representation LearningCode1
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Benchmark Results

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