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Creators/Authors contains: "Taha, Diaaeldin"

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  1. ; ; ; ; (, International Conference on Learning Representations 2025)
    Topological deep learning (TDL) has emerged as a powerful tool for modeling higher-order interactions in relational data. However, phenomena such as over- squashing in topological message-passing remain understudied and lack theoreti- cal analysis. We propose a unifying axiomatic framework that bridges graph and topological message-passing by viewing simplicial and cellular complexes and their message-passing schemes through the lens of relational structures. This ap- proach extends graph-theoretic results and algorithms to higher-order structures, facilitating the analysis and mitigation of oversquashing in topological message- passing networks. Through theoretical analysis and empirical studies on simplicial networks, we demonstrate the potential of this framework to advance TDL. 
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    Free, publicly-accessible full text available January 22, 2026