Dynamic Influence Maximization via Network Representation Learning

Influence maximization is a hot research topic in the social computing field and has gained tremendous studies motivated by its wild application scenarios. As the structures of social networks change over time, how to seek seed node sets from dynamic networks has attracted some attention. However, a...

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Bibliographic Details
Published inFrontiers in physics Vol. 9
Main Authors Sheng, Wei, Song, Wenbo, Li, Dong, Yang, Fei, Zhang, Yatao
Format Journal Article
LanguageEnglish
Published Frontiers Media S.A 22.02.2022
Subjects
dynamic social networks
efficiency
incremental updating
influence maximization
network representation learning
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Summary:Influence maximization is a hot research topic in the social computing field and has gained tremendous studies motivated by its wild application scenarios. As the structures of social networks change over time, how to seek seed node sets from dynamic networks has attracted some attention. However, all of the existing studies were based on network topology structure data which have the limitations of high dimensionality and low efficiency. Aiming at this drawback, we first convert each node in the network to a low-dimensional vector representation by network representation learning and then solve the problem of dynamic influence maximization in the low-dimensional latent space. Comprehensive experiments on NetHEPT, Twitter, UCI, and Wikipedia datasets show that our method can achieve influence diffusion performance similar to state-of-the-art approaches in much less time.
ISSN:2296-424X
2296-424X
DOI:10.3389/fphy.2021.827468