A Novel Centrality of Influential Nodes Identification in Complex Networks

Influential nodes identification in complex networks is vital for understanding and controlling the propagation process in complex networks. Some existing centrality measures ignore the impacts of neighbor node. It is well-known that degree is a famous centrality measure for influential nodes identi...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.58742-58751
Main Authors: Yang, Yuanzhi, Wang, Xing, Chen, You, Hu, Min, Ruan, Chengwei
Format: Article
Language:English
Subjects:
Clustering
clustering coefficient
Complex networks
degree centrality
Entropy
Indexes
influential nodes
Integrated circuits
neighbor node
Networks
Nodes
Object recognition
Time measurement
Volume measurement
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Summary:Influential nodes identification in complex networks is vital for understanding and controlling the propagation process in complex networks. Some existing centrality measures ignore the impacts of neighbor node. It is well-known that degree is a famous centrality measure for influential nodes identification, and the contributions of neighbors also should be taken into consideration. Furthermore, topological connections among neighbors will affect nodes' spreading ability, that is, the denser the connections among neighbors, the greater the chance of infection. In this paper, we propose a novel centrality, called DCC, to identify influential nodes by comprehensively considering degree and clustering coefficient as well as neighbors. The weights of degree and clustering coefficient are calculated by entropy technology. To verify the feasibility and effectiveness of DCC, the comparisons between DCC and other centrality measures in four aspects are conducted based on four real networks. The experimental results demonstrate that DCC is more effective in identifying influential nodes.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2983053