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- Title
融合级联活跃转发者的社交网络影响最大化方法.
- Authors
杨书新; 林仁耀; 许景峰; 梁文
- Abstract
The existing works for solving the problem of influence maximization mainly focused on graph and didn’t fully exploited the information cascade, which can’t effectively capture the actual influence between users. To this end, this paper proposed a new approach integrating active information forwarder for influence maximization based on the information cascade. Firstly, this approach designed an embedded neural network model considering active information forwarder and obtained the feature vectors of users by training model supervised by the actual diffusion record of the cascade data. Then, it presented a measurement method of user influence combining the feature vector of active information forwarder and information initiator according to the number of information reachable objects and diffusion probability. Finally, it selected the seeds by using greedy policy. The experimental results with four approaches on three large-scale data sets show the validity of this proposed approach in the actual spread of information.
- Subjects
PROBLEM solving; DATA recorders &; recording; SOCIAL networks; SEEDS; SUPERVISED learning; PROBABILITY theory
- Publication
Application Research of Computers / Jisuanji Yingyong Yanjiu, 2022, Vol 39, Issue 11, p3252
- ISSN
1001-3695
- Publication type
Article
- DOI
10.19734/j.issn.1001-3695.2022.04.0186