Fragmenting networks by targeting collective influencers at a mesoscopic level

Teruyoshi Kobayashi, Naoki Masuda*

*この研究の対応する著者

研究成果: Article査読

7 被引用数 (Scopus)

抄録

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure.

本文言語English
論文番号37778
ジャーナルScientific reports
6
DOI
出版ステータスPublished - 2016 11 25
外部発表はい

ASJC Scopus subject areas

  • 一般

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