67677

Автор(ов): 

2

Параметры публикации
Тип публикации: 
Доклад
Название: 
The PageRank Vector of a Scale-Free Web Network Growing by Preferential Attachment
ISBN/ISSN: 
978-3-030-92506-2
DOI: 
10.1007/978-3-030-92507-9_3
Наименование конференции: 
24rd International Conference on Distributed Computer and Communication Networks: Control, Computation, Communications (DCCN-2021)
Наименование источника: 
Lecture Notes in Computer Science
Обозначение и номер тома: 
Vol. 13144
Город: 
Berlin/Heidelberg, Germany
Издательство: 
Springer
Год издания: 
2021
Страницы: 
24-31
Аннотация
We consider a scale-free model of the Web network that is evolving by preferential attachment schemes and derive an explicit formula of its PageRank vector. Its $i^{th}$ element indicates the probability that a surfer resides at a related Web page $i$ in a stationary regime of an associated random walk. Considering the growth of a directed Web graph, we apply linear preferential attachment schemes proposed by Samorodnitsky et al. (2016). To express the probability of a connection between two nodes of this Web graph, our derivation allows us to avoid the consideration of complicated paths with random lengths and to cover both self-loops and multiple edges between nodes. An algorithm of the PageRank vector calculation for graphs without loops is provided. The approach can be extended in a similar way to graphs with loops. In this way, our approach enhances existing analysis schemes. It provides a better insight on the PageRank of growing scale-free Web networks and supports the adaptation of the model to gathered network statistics.
Библиографическая ссылка: 
Маркович Н.М., Krieger U. R. The PageRank Vector of a Scale-Free Web Network Growing by Preferential Attachment / Lecture Notes in Computer Science. Berlin/Heidelberg, Germany: Springer, 2021. Vol. 13144. С. 24-31.