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Found 2 papers, 2 papers with code
Multi-View Graph Representation Learning Beyond Homophily
However, partially due to the irregular non-Euclidean data in graphs, the pretext tasks are generally designed under homophily assumptions and cornered in the low-frequency signals, which results in significant loss of other signals, especially high-frequency signals widespread in graphs with heterophily.
Graph Representation Learning Beyond Node and Homophily
Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding.
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