Research map / Graph learning / Knowledge and temporal graphs
NeuroH-TGL: Neuro-Heterogeneity Guided Temporal Graph Learning Strategy for Brain Disease Diagnosis
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Proposes NeuroH-TGL, a temporal graph learning strategy that models heterogeneous neural nodes in dynamic functional brain networks for brain disease diagnosis.
Problem
Diagnosing brain diseases from dynamic functional brain networks (DFBNs) while accounting for heterogeneous neural nodes with extensive connections and drastic temporal changes, and their dependence on the brain's historical states.
Approach
Guides temporal graph learning with neuro-heterogeneity, treating nodes with more extensive connections and larger temporal changes differently and modeling how their spatio-temporal patterns are shaped by historical brain states (details not available in the abstract).
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