A multi-level graph contrastive framework with adaptive self-weighting outperforms prior single-level and multi-task GSSL methods on classification, clustering, and link prediction.
Collective classification in network data
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A Unified Perspective for Learning Graph Representations Across Multi-Level Abstractions
A multi-level graph contrastive framework with adaptive self-weighting outperforms prior single-level and multi-task GSSL methods on classification, clustering, and link prediction.