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 926–950 of 982 papers

TitleStatusHype
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast—0
Self-Supervised Graph Representation Learning via Global Context Prediction—0
Self-supervised Graph Representation Learning via Bootstrapping—0
Self-supervised Graph Representation Learning for Black Market Account Detection—0
Self-supervised Learning and Graph Classification under Heterophily—0
Self-Supervised Graph Representation Learning for Neuronal Morphologies—0
Self-supervision meets kernel graph neural models: From architecture to augmentations—0
Semantic Communication Enhanced by Knowledge Graph Representation Learning—0
Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition—0
Semantic Random Walk for Graph Representation Learning in Attributed Graphs—0
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees—0
Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease—0
SGA: A Graph Augmentation Method for Signed Graph Neural Networks—0
SGR: Self-Supervised Spectral Graph Representation Learning—0
Shedding Light on Problems with Hyperbolic Graph Learning—0
Siamese Attribute-missing Graph Auto-encoder—0
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning—0
SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning—0
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration—0
Simple yet Effective Gradient-Free Graph Convolutional Networks—0
Simple yet Effective Graph Distillation via Clustering—0
SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs—0
Sparse Decomposition of Graph Neural Networks—0
Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference—0
Spatio-Temporal Graph Representation Learning for Fraudster Group Detection—0
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Benchmark Results

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