TY - GEN
T1 - Identify influential spreaders in complex real-world networks
AU - Liu, Ying
AU - Tang, Ming
AU - Yue, Jing
AU - Gong, Jie
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/7/20
Y1 - 2016/7/20
N2 - Identifying the most influential spreaders in a complex network is important in optimizing the use of available resource and controlling spreading behaviors on it. Centrality is usually used to measure the importance of a node within the network, such as degree, betweenness, closeness, eigenvector, k-core, etc. Here considering the local connection pattern of nodes in the network structure, we propose a new centrality measure which is based not only on the nearest neighborhood of a node, but also on its 2-step and 3-step neighbors. To evaluate its effectiveness, we use the classic spreading model to simulate the spreading efficiency of nodes in the network and compare the performance of the proposed centrality with the most widely used centrality of degree and coreness in ranking spreaders. Results show that the proposed centrality is a much more accurate measure to predict spreading capability of nodes in real-world networks.
AB - Identifying the most influential spreaders in a complex network is important in optimizing the use of available resource and controlling spreading behaviors on it. Centrality is usually used to measure the importance of a node within the network, such as degree, betweenness, closeness, eigenvector, k-core, etc. Here considering the local connection pattern of nodes in the network structure, we propose a new centrality measure which is based not only on the nearest neighborhood of a node, but also on its 2-step and 3-step neighbors. To evaluate its effectiveness, we use the classic spreading model to simulate the spreading efficiency of nodes in the network and compare the performance of the proposed centrality with the most widely used centrality of degree and coreness in ranking spreaders. Results show that the proposed centrality is a much more accurate measure to predict spreading capability of nodes in real-world networks.
KW - Centrality
KW - Dynamic spreading
KW - Influential spreader
KW - Ranking
UR - https://www.scopus.com/pages/publications/84983413982
U2 - 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.209
DO - 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.209
M3 - 会议稿件
AN - SCOPUS:84983413982
T3 - Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015
SP - 1144
EP - 1148
BT - Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015
A2 - Ma, Jianhua
A2 - Li, Ali
A2 - Ning, Huansheng
A2 - Yang, Laurence T.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015
Y2 - 10 August 2015 through 14 August 2015
ER -