Non-backtracking cycles: length spectrum theory and graph mining applications

Non-backtracking cycles: length spectrum theory and graph mining applications

Abstract Graph distance and graph embedding are two fundamental tasks in graph mining. For graph distance, determining the structural dissimilarity between networks is an ill-defined problem, as there is no canonical way to compare two networks. Indeed, many of the existing approaches for network co...

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Bibliographische Detailangaben
Hauptverfasser: Leo Torres, Pablo Suárez-Serrato, Tina Eliassi-Rad
Format: Artikel
Sprache:English
Veröffentlicht: SpringerOpen 2019-06-01
Schriftenreihe:Applied Network Science
Schlagworte:
Graph distance
Graph embedding
Algebraic topology
Length spectrum
Online Zugang:http://link.springer.com/article/10.1007/s41109-019-0147-y

Online

http://link.springer.com/article/10.1007/s41109-019-0147-y

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