Identifying localized influential spreaders of information spreading

Autor:	Hui Tian, Xuzhen Zhu, Wei Wang, Xiang-Chun Liu, Zeng-Ping Zhang
Rok vydání:	2019
Předmět:	Statistics and Probability Computer science Node (networking) 0103 physical sciences Network science Data mining Complex network 010306 general physics Condensed Matter Physics computer.software_genre 01 natural sciences computer 010305 fluids & plasmas
Zdroj:	Physica A: Statistical Mechanics and its Applications. 519:92-97
ISSN:	0378-4371
DOI:	10.1016/j.physa.2018.11.045
Popis:	Identifying the influential spreaders of information spreading dynamics is a hot topic in the field of network science. To identify the influential spreaders, most previous studies were based on the global information of the network. In this paper, we propose a strategy for identifying the influential spreaders from a randomly selected initial-seed node. The seeds are connected as a chain, and are localized to the initial-seed. In our proposed preferentially random walk based influential spreaders identifying strategy, the walker’s movement is adjusted by neighbors’ degrees. The seeds are those nodes that the walker ever visited. Through extensive numerical simulations on artificial networks and four real-world networks, we find that selecting large degree nodes preferentially is more likely to find the most influential spreaders. The outbreak threshold decreases when preferentially select hubs. Our results shed some light into identifying the most localized influential spreaders.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::05860c341eef6cf6f7e6f6dd73e29476 https://doi.org/10.1016/j.physa.2018.11.045 Zobrazit plný text záznamu Full Text from ScienceDirect