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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 261–270 of 982 papers

TitleStatusHype
A bi-diffusion based layer-wise sampling method for deep learning in large graphs—0
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning—0
Distribution Preserving Graph Representation Learning—0
Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure—0
Generalized Laplacian Positional Encoding for Graph Representation Learning—0
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs—0
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs—0
A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning—0
A Class-Aware Representation Refinement Framework for Graph Classification—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
Show:102550
← PrevPage 27 of 99Next →

Benchmark Results

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