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 6170 of 982 papers

TitleStatusHype
Expander Graph PropagationCode1
An Open Challenge for Inductive Link Prediction on Knowledge GraphsCode1
CCGL: Contrastive Cascade Graph LearningCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
Large-Scale Representation Learning on Graphs via BootstrappingCode1
A Representation Learning Framework for Property GraphsCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Boosting Graph Structure Learning with Dummy NodesCode1
Show:102550
← PrevPage 7 of 99Next →

Benchmark Results

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