Signed-PageRank: An Efficient Influence Maximization Framework for Signed Social Networks

Influence maximization in social networks is of great importance for marketing new products. Signed social networks with both positive (friends) and negative (foes) relationships pose new challenges and opportunities, since the influence of negative relationships can be leveraged to promote informat...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2021-05, Vol.33 (5), p.2208-2222
Main Authors: Yin, Xiaoyan, Hu, Xiao, Chen, Yanjiao, Yuan, Xu, Li, Baochun
Format: Article
Language:English
Subjects:
Adaptation models
Advertisements
Diseases
Greedy algorithms
Heuristic algorithms
influence maximization
Information dissemination
information propagation
Integrated circuit modeling
Maximization
Optimization
recommendation
Search algorithms
Signed social networks
Social networking (online)
Social networks
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Summary:Influence maximization in social networks is of great importance for marketing new products. Signed social networks with both positive (friends) and negative (foes) relationships pose new challenges and opportunities, since the influence of negative relationships can be leveraged to promote information propagation. In this paper, we study the problem of influence maximization for advertisement recommendation in signed social networks. We propose a new framework to characterize the information propagation process in signed social networks, which models the dynamics of individuals' beliefs and attitudes towards the advertisement based on recommendations from both positive and negative neighbours. To achieve influence maximization in signed social networks, we design a novel Signed-PageRank ({\sf SPR} SPR ) algorithm, which selects the initial seed nodes by jointly considering their positive and negative connections with the rest of the network. Our extensive experimental results confirm that our proposed {\sf SPR} SPR algorithm can effectively and efficiently influence a broader range of individuals in the signed social networks than benchmark algorithms on both synthetic and real datasets.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2019.2947421