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	トポロジカル深層学習	Mapperアルゴリズム
分野	位相幾何学	位相幾何学
系統	Machine learning	Machine learning
提唱年	2023	2007
提唱者	Topological deep learning literature	Singh, Mémoli & Carlsson
種類	Higher-order message-passing framework	Graph-based topological summarization
原典	Hajij, M., et al. (2023). Topological deep learning: Going beyond graph data. arXiv preprint.	Singh, G., Mémoli, F., & Carlsson, G. (2007). Topological methods for the analysis of high dimensional data sets and 3D object recognition. Eurographics Symposium on Point-Based Graphics, 91–100.
別名	TDL, Topological Neural Networks, Higher-Order Deep Learning, Topolojik Derin Öğrenme	Topological Mapper, TDA Mapper, Reeb Graph Approximation, Eşleyici Algoritma
関連	3	2
概要	Topological Deep Learning (TDL) is a framework that extends deep learning beyond graphs to higher-order topological domains such as simplicial complexes, cell complexes, and hypergraphs. Formalized by Hajij et al. (2023), TDL provides a unified mathematical language for defining message-passing schemes across cells of different ranks, enabling neural networks to model multi-way interactions that pairwise graph edges cannot capture. It is relevant to researchers working with relational, geometric, or biological data exhibiting group-level dependencies.	The Mapper algorithm is a method in topological data analysis (TDA) that produces a graph-based summary of the shape of high-dimensional point cloud data. Introduced by Singh, Mémoli, and Carlsson in 2007 at the Eurographics Symposium on Point-Based Graphics, Mapper constructs a simplicial complex — typically a graph — that captures the global topological and geometric structure of a dataset without requiring a fixed embedding or metric assumption.