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

Showing 7180 of 982 papers

TitleStatusHype
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
Data Augmentation on Graphs: A Technical SurveyCode1
A step towards neural genome assemblyCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
Adversarial Graph DisentanglementCode1
Large-Scale Representation Learning on Graphs via BootstrappingCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Certifiably Robust Graph Contrastive LearningCode1
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Benchmark Results

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