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Steering the spread of influence adaptively in social networks via a discrete scheduled particle swarm optimization

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Abstract

The classical influence maximization (IM) in social network analysis aims at selecting a group of influential nodes as seed so that the number of nodes activated by the set is maximized when the spreading process completes. The single stage seeding problem would ignite all the seeds at the very beginning of the diffusion and let the influence diffuses passively with no additional supervision. However, the effect of scheduled seeding activation operations on improving the marginal profit tends to be ignored. More importantly, the practical scenarios that need external seeding activities can not be well depicted. In this paper, seed activation strategies are investigated for the adaptive influence maximization (AIM) problem under general feedback models. A novel bio-inspired meta-heuristic policy named discrete scheduled particle swarm optimization (DSPSO) is proposed to tackle the intractable problem confronted by AIM, i.e., which nodes need to be ignited at the right time during the process of influence spreading? Following the framework, the proposed policy selects optimal size of nodes into the seed set in each round to ensure the continuation of the spreading process. Dynamic encoding mechanism for the particle individuals is constructed. To make a full exploration of the solution space, a local search strategy specifically for the discrete network topology is implemented on the historical best individuals of the swarm. Extensive experiments on four social networks under different activation feedback models demonstrate the advantage of the scheduled seeding mechanism over the single stage seeding approaches, and show that the proposed DSPSO outperforms the existing greedy strategy as well as the topological heuristics.

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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. http://snap.stanford.edu/data/index.html

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Acknowledgements

This work was financially supported by the Gansu Provincial Science Fund for Distinguished Young Scholars under grant number 23JRRA766, the Lanzhou University of Technology Fund for Outstanding Young Scholars, the Natural Science Foundation of Zhejiang Provincial under grant number LQ20F020011, the National Natural Science Foundations of China under grant number 62162040, and the National Key Research and Development Plan under grant number 2020YFB1713600.

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  1. School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, China

    Jianxin Tang, Shihui Song, Jimao Lan, Li Zhang & Fuqing Zhao

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Shihui Song, Jimao Lan, Li Zhang and Fuqing Zhao contributed equally to this work.

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Tang, J., Song, S., Lan, J. et al. Steering the spread of influence adaptively in social networks via a discrete scheduled particle swarm optimization. Appl Intell 53, 25070–25091 (2023). https://doi.org/10.1007/s10489-023-04884-y

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