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 201–210 of 982 papers

TitleStatusHype
Graph Autoencoder for Graph Compression and Representation LearningCode1
Graph Barlow Twins: A self-supervised representation learning framework for graphsCode1
Graph-based prediction of Protein-protein interactions with attributed signed graph embeddingCode1
PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation modelsCode1
When Do Flat Minima Optimizers Work?Code1
Graph Representation Learning for Multi-Task Settings: a Meta-Learning ApproachCode1
Graph Trend Filtering Networks for RecommendationsCode1
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
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Benchmark Results

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