@inproceedings{b1b5b375dc3e41b892019d1c6d3f6e7d,
title = "Identifying influential nodes in complex networks: A multiple attributes fusion method",
abstract = "How to identify influential nodes is still an open hot issue in complex networks. Lots of methods (e.g., degree centrality, betweenness centrality or K-shell) are based on the topology of a network. These methods work well in scale-free networks. In order to design a universal method suitable for networks with different topologies, this paper proposes a Multiple Attribute Fusion (MAF) method through combining topological attributes and diffused attributes of a node together. Two fusion strategies have been proposed in this paper. One is based on the attribute union (FU), and the other is based on the attribute ranking (FR). Simulation results in the Susceptible-Infected (SI) model show that our proposed method gains more information propagation efficiency in different types of networks.",
author = "Lu Zhong and Chao Gao and Zili Zhang and Ning Shi and Jiajin Huang",
year = "2014",
doi = "10.1007/978-3-319-09912-5_2",
language = "英语",
isbn = "9783319099118",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "11--22",
booktitle = "Active Media Technology - 10th International Conference, AMT 2014, Proceedings",
note = "10th International Conference on Active Media Technology, AMT 2014 ; Conference date: 11-08-2014 Through 14-08-2014",
}