Open AccessIdentifying Influential Nodes in Complex Networks Based on Local and Global Methods
Author(s) -
Li Mijia,
Hongquan Wei,
Yingle Li,
Shuxin Liu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1738/1/012026
Subject(s) - betweenness centrality , computer science , complex network , core (optical fiber) , decomposition method (queueing theory) , decomposition , algorithm , theoretical computer science , mathematics , centrality , discrete mathematics , telecommunications , ecology , combinatorics , world wide web , biology
Identifying Influential Nodes in complex networks is of great significance in both theory and reality. K-shell decomposition method is a local method which is suitable for increasing scale of complex networks but limited in accuracy because many nodes are partitioned with the same K-shell value. To overcome the coarse result of K-shell, an improved K-shell which considers the number of nodes’ iteration layers and degrees is proposed. Unlike local methods, global methods such as Betweenness Centralities (BC) are accurate but time-consuming. We employed an algorithm framework which combines advantages of both local and global methods where core network is extracted by improved K-shell and then BC is used to quantitatively analyze nodes in the core network. We compare the proposed method with other existing methods on Susceptible-Infective-Removal (SIR) mode. Experiments on three real networks show that the proposed method is more efficient and accurate.