Target-Aware Holistic Influence Maximization in Spatial Social Networks

Article


Cai, Taotao, Li, Jianxin, Mian, Ajmal, Li, Rong-Hua, Sellis, Timos and Yu, Jeffrey Xu. 2022. "Target-Aware Holistic Influence Maximization in Spatial Social Networks ." IEEE Transactions on Knowledge and Data Engineering. 34 (4), pp. 1993-2007. https://doi.org/10.1109/TKDE.2020.3003047
Article Title

Target-Aware Holistic Influence Maximization in Spatial Social Networks

ERA Journal ID17876
Article CategoryArticle
AuthorsCai, Taotao, Li, Jianxin, Mian, Ajmal, Li, Rong-Hua, Sellis, Timos and Yu, Jeffrey Xu
Journal TitleIEEE Transactions on Knowledge and Data Engineering
Journal Citation34 (4), pp. 1993-2007
Number of Pages15
Year2022
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1041-4347
1558-2191
Digital Object Identifier (DOI)https://doi.org/10.1109/TKDE.2020.3003047
Web Address (URL)https://ieeexplore.ieee.org/document/9119847
AbstractInfluence 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.
KeywordsHolistic influence maximization
Related Output
Is supplemented byhttps://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/LP180100750
Is supplemented byhttps://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DP190102443
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460503. Data models, storage and indexing
Public NotesFiles associated with this item cannot be displayed due to copyright restrictions.
Byline AffiliationsDeakin University
University of Western Australia
Beijing Institute of Technology, China
Swinburne University of Technology
Chinese University of Hong Kong, China
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