60632

Автор(ы): 

Автор(ов): 

2

Параметры публикации

Тип публикации: 

Доклад

Название: 

Measuring Proximity in Attributed Networks for Community Detection

DOI: 

10.1007/978-3-030-65347-7_3

Наименование конференции: 

  • 9th International Conference on Complex Networks and Their Applications (Basel, Switzerland, 2020)

Наименование источника: 

  • Proceeding of the 9th International Conference on Complex Networks and Their Applications (Basel, Switzerland, 2020)

Обозначение и номер тома: 

V. 1

Город: 

  • Basel, Switzerland

Издательство: 

  • Springer Nature

Год издания: 

2020

Страницы: 

27-37
Аннотация
Proximity measures on graphs have a variety of applications in network analysis, including community detection. Previously they have been mainly studied in the context of networks without attributes. If node attributes are taken into account, however, this can provide more insight into the network structure. In this paper, we extend the definition of some well-studied proximity measures to attributed networks. To account for attributes, several attribute similarity measures are used. Finally, the obtained proximity measures are applied to detect the community structure in some real-world networks using the spectral clustering algorithm.

Библиографическая ссылка: 

Айнулин Р.Р., Чеботарев П.Ю. Measuring Proximity in Attributed Networks for Community Detection / Proceeding of the 9th International Conference on Complex Networks and Their Applications (Basel, Switzerland, 2020). Basel, Switzerland: Springer Nature, 2020. V. 1. С. 27-37.