<ul><li>Self-supervised topological deep learning (TDL) is an emerging field with potential for modeling higher-order interactions in cellular complexes to derive representations of unlabeled graphs.</li><li>Cellular complexes have more expressive power compared to simplicial complexes, but self-supervised learning in this domain faces challenges like extrinsic structural constraints and intrinsic semantic redundancy.</li><li>CellCLAT (Cellular Complex Contrastive Learning with Adaptive Trimming) is introduced to address these challenges by preserving cellular topology and reducing informational redundancy through parameter perturbation-based augmentation and cellular trimming scheduler.</li><li>CellCLAT shows significant improvements over existing self-supervised graph learning methods according to theoretical justifications and empirical validations.</li></ul>

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning

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