Identifying influential nodes based on network representation learning in complex networks

被引:27
作者
Wei, Hao [1 ]
Pan, Zhisong [1 ]
Hu, Guyu [1 ]
Zhang, Liangliang [1 ]
Yang, Haimin [1 ]
Li, Xin [1 ]
Zhou, Xingyu [1 ]
机构
[1] Army Engn Univ PLA, Coll Command Informat Syst, Nanjing, Jiangsu, Peoples R China
来源
PLOS ONE | 2018年 / 13卷 / 07期
关键词
CENTRALITY; SPREADERS; COMMUNITY; RANKING; IDENTIFICATION;
D O I
10.1371/journal.pone.0200091
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Identifying influential nodes is an important topic in many diverse applications, such as accelerating information propagation, controlling rumors and diseases. Many methods have been put forward to identify influential nodes in complex networks, ranging from node centrality to diffusion-based processes. However, most of the previous studies do not take into account overlapping communities in networks. In this paper, we propose an effective method based on network representation learning. The method considers not only the overlapping communities in networks, but also the network structure. Experiments on real-world networks show that the proposed method outperforms many benchmark algorithms and can be used in large-scale networks.
引用
收藏
页数:13
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