TY - JOUR
T1 - Identify influential spreaders in complex networks, the role of neighborhood
AU - Liu, Ying
AU - Tang, Ming
AU - Zhou, Tao
AU - Do, Younghae
N1 - Publisher Copyright:
© 2016 Elsevier B.V. All rights reserved.
PY - 2016/6/15
Y1 - 2016/6/15
N2 - Identifying the most influential spreaders is an important issue in controlling the spreading processes in complex networks. Centrality measures are used to rank node influence in a spreading dynamics. Here we propose a node influence measure based on the centrality of a node and its neighbors' centrality, which we call the neighborhood centrality. By simulating the spreading processes in six real-world networks, we find that the neighborhood centrality greatly outperforms the basic centrality of a node such as the degree and coreness in ranking node influence and identifying the most influential spreaders. Interestingly, we discover a saturation effect in considering the neighborhood of a node, which is not the case of the larger the better. Specifically speaking, considering the 2-step neighborhood of nodes is a good choice that balances the cost and performance. If further step of neighborhood is taken into consideration, there is no obvious improvement and even decrease in the ranking performance. The saturation effect may be informative for studies that make use of the local structure of a node to determine its importance in the network.
AB - Identifying the most influential spreaders is an important issue in controlling the spreading processes in complex networks. Centrality measures are used to rank node influence in a spreading dynamics. Here we propose a node influence measure based on the centrality of a node and its neighbors' centrality, which we call the neighborhood centrality. By simulating the spreading processes in six real-world networks, we find that the neighborhood centrality greatly outperforms the basic centrality of a node such as the degree and coreness in ranking node influence and identifying the most influential spreaders. Interestingly, we discover a saturation effect in considering the neighborhood of a node, which is not the case of the larger the better. Specifically speaking, considering the 2-step neighborhood of nodes is a good choice that balances the cost and performance. If further step of neighborhood is taken into consideration, there is no obvious improvement and even decrease in the ranking performance. The saturation effect may be informative for studies that make use of the local structure of a node to determine its importance in the network.
KW - Epidemic spreading
KW - Influential spreader
KW - Neighborhood centrality
KW - Saturation effect
UR - http://www.scopus.com/inward/record.url?scp=84959266380&partnerID=8YFLogxK
U2 - 10.1016/j.physa.2016.02.028
DO - 10.1016/j.physa.2016.02.028
M3 - Article
AN - SCOPUS:84959266380
SN - 0378-4371
VL - 452
SP - 289
EP - 298
JO - Physica A: Statistical Mechanics and its Applications
JF - Physica A: Statistical Mechanics and its Applications
ER -