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
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
Deep Graph Contrastive Representation LearningCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
Boosting Graph Structure Learning with Dummy NodesCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
A Gentle Introduction to Deep Learning for GraphsCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
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Benchmark Results

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