Author:
Fujisawa Koudai,Kumano Masahito,Kimura Masahiro
Abstract
AbstractAiming at knowledge discovery for temporal sequences of cooking recipes published in social media platforms from the viewpoint of network science, we consider an analysis of temporal higher-order networks of ingredients derived from such recipe streams by focusing on the framework of simplicial complex. Previous work found interesting properties of temporal simplicial complexes for the human proximity interactions in five different social settings by examining the configuration transitions before and after triplet interaction events corresponding to 2-simplices. In this paper, as an effective extension of the previous work to the case of higher dimensionaln-simplices corresponding to newly published recipes, we propose a novel method of configuration transition analysis by incorporating the following two features. First, to focus on changes in the topological structure of temporal simplicial complex, we incorporate analyzing the transitions of boundary-based configurations. Next, to focus on the temporal heterogeneity in usage activities of ingredients, we incorporate analyzing the transitions of active configurations by introducing the activity degree of configuration. Using real data of a Japanese recipe sharing site, we empirically evaluate the effectiveness of the proposed method, and reveal some characteristics of the temporal evolution of Japanese homemade recipes published in social media from the perspective of ingredient co-occurrences.
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Computer Networks and Communications,Multidisciplinary
Reference28 articles.
1. Ahn Y-Y, Ahnert S-E, Bagrow J-P, Barabási A-L (2011) Flavor network and the principles of food pairing. Sci Rep 1:196–11967
2. Barabási A-L (2016) Network science. Cambridge University Press, Cambridge
3. Benson A-R, Abebe R, Schaub M-T, Jadbabaie A, Kleinberg J (2019) Simplicial closure and higher-order link prediction. PNAS 115(48):11221–11230
4. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
5. Bobrowski O, Krioukov D (2022) Random simplicial complexes: models and phenomena. In: Battiston F, Petri G (eds) Higher-Order systems. Springer, Cham, pp 59–96
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献