Details

Autor(en) / Beteiligte

• Zhang, Haonan
• Fu, Luoyi
• Ding, Jiaxin
• Tang, Feilong
• Xiao, Yao
• Wang, Xinbing
• Chen, Guihai
• Zhou, Chenghu

Titel

Maximizing the Spread of Effective Information in Social Networks

Ist Teil von

• IEEE transactions on knowledge and data engineering, 2023-04, Vol.35 (4), p.4062-4076

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2023

Quelle

IEL

Beschreibungen/Notizen

• Influence maximization through social networks has aroused tremendous interests nowadays. However, people's various expressions or feelings about a same idea often cause ambiguity via word of mouth. Consequently, the problem of how to maximize the spread of "effective information" still remains largely open. In this paper, we consider a practical setting where ideas can deviate from their original version to invalid forms during message passing, and make the first attempt to seek a union of users that maximizes the spread of effective influence, which is formulated as an Influence Maximization with Information Variation (IMIV) problem. To this end, we model the information as a vector, and quantify the difference of two arbitrary vectors as a distance by a matching function. We further establish a process where such distance increases with the propagation and ensure the recipient whose vector distance is less than a threshold can be effectively influenced. Due to the NP-hardness of IMIV, we greedily select users that can approximately maximize the estimation of effective propagation. Especially, for networks of small scales, we derive a condition under which all the users can be effectively influenced. Our models and theoretical findings are further consolidated through extensive experiments on real-world datasets.