Targeting Influential Nodes for Recovery in Bootstrap Percolation on Hyperbolic Networks

被引：4

作者：

Marshall, Christine ^{[1
]}

O'Riordan, Colm ^{[1
]}

Cruickshank, James ^{[2
]}

机构：

[1] Natl Univ Ireland, Discipline Informat Technol, Galway, Ireland

[2] Natl Univ Ireland, Sch Math, Galway, Ireland

来源：

NETWORK INTELLIGENCE MEETS USER CENTERED SOCIAL MEDIA NETWORKS | 2018年

关键词：

Bootstrap percolation; Bootstrap percolation with recovery; Hyperbolic random geometric graphs; MODEL;

D O I：

10.1007/978-3-319-90312-5_1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The influence of our peers is a powerful reinforcement for our social behaviour, evidenced in voter behaviour and trend adoption. Bootstrap percolation is a simple method for modelling this process. In this work we look at bootstrap percolation on hyperbolic random geometric graphs, which have been used to model the Internet graph, and introduce a form of bootstrap percolation with recovery, showing that random targeting of nodes for recovery will delay adoption, but this effect is enhanced when nodes of high degree are selectively targeted.

引用

页码：3 / 16

页数：14

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