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Multi-topical authority sensitive influence maximization with authority based graph pruning and three-stage heuristic optimization

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Abstract

Influence maximization is closely related to the interesting topic of users. However, it is often ignored by most previous researches. Even though topics are considered by some previous researches, they neglect users’ different authority on different topics. Only one work researches topic authority sensitive influence maximization for a given topic, but its time efficiency is low. What’s more, messages usually include more than one topic. In order to solve these problems, we propose a new Multi-Topical Authority sensitive Independent Cascade model (MTAIC), namely the Multi-Topical Authority sensitive Greedy algorithm (MTAG) optimized by the Authority Based Graph Pruning (AGP) and Three-stage Heuristic Optimization Strategy (THOS). A new metric, Influence Spread of seed set on Multi-Topics (ISMT), is put forward to measure the influence spread of seed set considering multi-topics information propagation simultaneously. We do extensive experiments to compare our algorithm with other baseline algorithms on two real-world datasets. Experimental results show that ISMT is an effective measure of influence maximization considering multi-topic authority. The experimental results also demonstrate the effectiveness of MTAIC model and MTAG-AGP, THOS-MTAIC algorithms in terms of ISMT and running time.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China under grants 2016YFB0800402 and 2016QY01W0202, National Natural Science Foundation of China under grants U1936108, U1836204, U1401258 and 61502185.

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Authors and Affiliations

  1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yuhua Li, Ruixuan Li, Xiaoqing Xiong, Xiwu Gu, Tianan Liang, Mingli Xu & Yumeng Yuan

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  1. Yuhua Li
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  2. Ruixuan Li
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  3. Xiaoqing Xiong
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Correspondence to Yuhua Li.

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Li, Y., Li, R., Xiong, X. et al. Multi-topical authority sensitive influence maximization with authority based graph pruning and three-stage heuristic optimization. Appl Intell 51, 8432–8450 (2021). https://doi.org/10.1007/s10489-021-02213-9

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