Persistent Path Homology of Directed Networks Samir Chowdhury and Facundo Mémoli
نویسنده
چکیده
While standard persistent homology has been successful in extracting information from metric datasets, its applicability to more general data, e.g. directed networks, is hindered by its natural insensitivity to asymmetry. We extend a construction of homology of digraphs due to Grigoryan, Lin, Muranov and Yau to the persistent framework. The result, which we call persistent path homology or PPH, encodes a rich level of detail about the asymmetric structure of the input directed network. For example, we prove that PPH identifies a class of directed cyclic networks as directed analogues of the circle. In general, PPH produces signatures that differ from natural extensions of Rips or Čech persistence to the directed setting, but we prove that PPH agrees with Čech persistence on symmetric spaces. Additionally, we prove that PPH agrees with Čech persistence on directed networks satisfying a local condition that we call square-freeness. We prove stability of PPH by utilizing a separate theory of homotopy of digraphs that is compatible with path homology. Finally, we study computational aspects of PPH, and derive an algorithm showing that over field coefficients, computing PPH requires the same worst case running time as standard persistent homology. E-mail address: [email protected],[email protected]. Date: July 13, 2017. PERSISTENT PATH HOMOLOGY OF DIRECTED NETWORKS 1
منابع مشابه
Persistent Path Homology of Directed Networks Samir Chowdhury and Facundo
While standard persistent homology has been successful in extracting information from metric datasets, its applicability to more general data, e.g. directed networks, is hindered by its natural insensitivity to asymmetry. We study a construction of homology of digraphs due to Grigor’yan, Lin, Muranov and Yau, and extend this construction to the persistent framework. The result, which we call pe...
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