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Topic relevance and temporal activity-aware influence maximization in social network

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Abstract

Influence maximization has attracted significant attention with the aim to find k individuals that can eventually maximize the influence individuals. The diverse applications include viral marketing, career prediction, and much more. In the classic influence maximization problem, most literature considers the dynamic of relationships between users on social networks, which results in high time complexity. Instead of relationships, from the perspective of users, we capture the dynamic through the temporal activity prediction of each user. In this paper, we introduce a Topic relevance and Temporal activity for the Influence Maximization (TTIM) problem, which is proved to be NP-hard in theory. We propose the Reverse Influence Set based framework for TTIM (RIS-TTIM), a quick, accurate, and unsupervised algorithm to solve the TTIM problem. This novel algorithm can obtain an approximation rate of (1-1/e - ε) under the general information diffusion model. Specifically, the RIS-TTIM algorithm is divided into two phases: (i) sampling topic relevance Reverse Reachable (RR) sets based on temporal activity degree, and (ii) selecting final influential nodes that can maximize the largest topic relevance nodes. Moreover, a novel method is proposed to predict the temporal activity degree of each user in the future. Experimental results on five real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm when compared with the baseline approaches.

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Acknowledgements

The work was supported in part by the Basic Research Program of Jiangsu Province (No. BK20191274).

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Authors and Affiliations

  1. College of Computer Science & Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Wei Jia, Weinan Niu, Li Yan & Zongmin Ma

  2. Department of Computer Science, University of Massachusetts Lowell, Lowell, MA, 01854, USA

    Ruizhe Ma

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  1. Wei Jia
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Correspondence to Ruizhe Ma.

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Jia, W., Ma, R., Niu, W. et al. Topic relevance and temporal activity-aware influence maximization in social network. Appl Intell 52, 16149–16167 (2022). https://doi.org/10.1007/s10489-022-03430-6

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