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 31–40 of 982 papers

TitleStatusHype
Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection—0
Local Distance-Preserving Node Embeddings and Their Performance on Random GraphsCode0
Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems—0
Robo-taxi Fleet Coordination at Scale via Reinforcement LearningCode1
LGIN: Defining an Approximately Powerful Hyperbolic GNNCode0
Node Embeddings via Neighbor Embeddings—0
Inductive Graph Representation Learning with Quantum Graph Neural Networks—0
MSNGO: multi-species protein function annotation based on 3D protein structure and network propagationCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
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Benchmark Results

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