Research map / Graph learning / Graph self-supervised learning
Self-Supervised Heterogeneous Graph Learning: a Homophily and Heterogeneity View
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Proposes a self-supervised heterogeneous graph learning method that addresses human-defined meta-paths and underused heterogeneity, as described in the truncated abstract.
Problem
Self-supervised heterogeneous graph learning relies on human-defined meta-paths to capture homophily, which requires expert knowledge and computation, and it underutilizes the heterogeneity of the graph.
Approach
Abstract only partially available; the proposed method is not described in the provided text, so the core mechanism is unknown beyond addressing meta-path dependence and underused heterogeneity.
Closest papers by meaning
- Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective (NeurIPS 2024 · similarity 0.91)
- HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport (ICML 2025 · similarity 0.91)
- HeterSEED: Semantics–Structure Decoupling for Heterogeneous Graph Learning under Heterophily (NeurIPS 2026 · similarity 0.90)
- HGDL: Heterogeneous Graph Label Distribution Learning (NeurIPS 2024 · similarity 0.89)
- On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks (NeurIPS 2024 · similarity 0.89)
- FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity (CVPR 2025 · similarity 0.89)
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