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Found 2 papers, 2 papers with code
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other.
Ranked #2 on
Node Classification
on wiki
Graph Learning
Node Classification on Non-Homophilic (Heterophilic) Graphs
Edge Proposal Sets for Link Prediction
Here, we demonstrate how simply adding a set of edges, which we call a \emph{proposal set}, to the graph as a pre-processing step can improve the performance of several link prediction algorithms.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
