Target-Aware Holistic Influence Maximization in Spatial Social Networks

Influence maximization has recently received significant attention for scheduling online campaigns or advertisements on social network platforms. However, most studies only focus on user influence via cyber interactions while ignoring their physical interactions which are also essential to gauge inf...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2022-04, Vol.34 (4), p.1993-2007
Main Authors: Cai, Taotao, Li, Jianxin, Mian, Ajmal, Li, Rong-Hua, Sellis, Timos, Yu, Jeffrey Xu
Format: Article
Language:English
Subjects:
Advertisements
Australia
Companies
Diffusion
Greedy algorithms
Holistic influence maximization
Indexes
Integrated circuit modeling
Maximization
NP-hard problem
Optimization
Queries
Social networking (online)
Social networks
spatial social networks
targeted advertisements
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Summary:Influence maximization has recently received significant attention for scheduling online campaigns or advertisements on social network platforms. However, most studies only focus on user influence via cyber interactions while ignoring their physical interactions which are also essential to gauge influence propagation. Additionally, targeted campaigns or advertisements have not received sufficient attention. To address these issues, we first devise a novel holistic influence diffusion model that takes into account both cyber and physical user interactions in an effective and practical way. Based on the new diffusion model, we formulate a new problem of holistic influence maximization , denoted as HIM query, for targeted advertisements in a spatial social network. The HIM query problem aims to find a minimum set of users whose holistic influence can cover all target users in the network, which belongs to a set covering problem. Since the HIM query problem is NP-hard, we develop a greedy baseline algorithm and then improve on this algorithm to reduce the computational cost. To deal with large networks, we also design a spatial-social index to maintain the social, spatial and textual information of users, as well as developing an index-based efficient solution. Finally, we conduct extensive experiments using one synthetic and three real-world datasets to validate the efficiency and effectiveness of the proposed holistic influence diffusion model and our developed algorithms.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2020.3003047