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 731–740 of 982 papers

TitleStatusHype
A Deep Latent Space Model for Graph Representation LearningCode0
ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural NetworksCode0
Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining—0
MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learningCode1
Directed Graph Embeddings in Pseudo-Riemannian Manifolds—0
Evaluating Modules in Graph Contrastive LearningCode1
Node Classification Meets Link Prediction on Knowledge Graphs—0
Graph Neural Networks for Natural Language Processing: A SurveyCode2
Do Transformers Really Perform Bad for Graph Representation?Code2
Self-supervised Graph-level Representation Learning with Local and Global StructureCode1
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Benchmark Results

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