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Time and value aware influence blocking maximization in geo-social networks

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Abstract

The influence blocking maximization (IBM) problem aims to identify the most influential set of positive nodes in a social network to prevent the propagation of negative information throughout the network. However, existing studies have not considered factors such as the time delay of influence propagation and the value factor of nodes. Therefore, we first propose an influence propagation model called Time-Delayed Campaign-Oblivious Independent Cascade Model (TD-COICM), which incorporates time delay to simulate the decay of influence over time in social networks. Additionally, we present the Time and Value aware Influence Blocking Maximization (TVIBM) problem by considering the value factor of nodes under TD-COICM. More precisely, the value of a node is determined by its social influence and geographical distance from Rumor Source (RS). We demonstrate TVIBM is NP-hard and the influence function is monotonic and submodular. To overcome the low efficiency of the greedy algorithm, we combine the technique of Reverse Influence Sampling (RIS) and present the Reverse Influence Sampling with Time Delay (RIS-TD) algorithm. We prove that the RIS-TD algorithm has a high probability to return a \(\left( 1-1/e-\epsilon \right) \) approximate guarantee. To improve the efficiency, we further present the ERIS-TD algorithm by reducing the number of samples. Experiments on three real geographical social networks further confirm that our algorithms outperform other baseline algorithms in effectiveness, and outperform RIS-based algorithms in efficiency.

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Data availibility

The three datasets used in this study are available in SNAP, http://snap.stanford.edu/data/. The Ego-Facebook dataset is available at http://snap.stanford.edu/data/ego-Facebook.html. The Brightkite dataset is available at http://snap.stanford.edu/data/loc-Brightkite.html. The Gowalla dataset is available at http://snap.stanford.edu/data/loc-Gowalla.html.

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Acknowledgements

This study was funded by the Fundamental Research Funds for the Universities of Heilongjiang (Nos. 145109217, 135509234), and the Youth Science and Technology Innovation Personnel Training Project of Heilongjiang (No. UNPYSCT-2020072)

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Author notes

  1. Wenlong Zhu and Chongyuan Peng have made equal contributions to this paper.

Authors and Affiliations

  1. College of Computer and Control Engineering, Qiqihar University, Wenhua Street, Qiqihar, 161006, Heilongjiang, China

    Wenlong Zhu, Chongyuan Peng, Yu Miao, Yufan Bai & Yingchun Diao

  2. Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihar University, Wenhua Street, Qiqihar, 161006, Heilongjiang, China

    Wenlong Zhu

  3. College of Teacher Education, Qiqihar University, Wenhua Street, Qiqihar, 161006, Heilongjiang, China

    Shuangshuang Yang

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  1. Wenlong Zhu
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Contributions

Wenlong Zhu contributed to Conceptualization; Wenlong Zhu, Chongyuan Peng contributed to Methodology; Chongyuan Peng contributed to Formal analysis and investigation; Chongyuan Peng contributed to Writing—original draft preparation; Wenlong Zhu contributed to Writing—review and editing; Wenlong Zhu, Shuangshuang Yang contributed to Funding acquisition; Yufan Bai, Yingchun Diao contributed to Resources; Shuangshuang Yang contributed to Supervision.

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Correspondence to Wenlong Zhu.

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Zhu, W., Peng, C., Miao, Y. et al. Time and value aware influence blocking maximization in geo-social networks. J Supercomput 80, 21149–21178 (2024). https://doi.org/10.1007/s11227-024-06252-0

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