Abstract
The influence maximization (IM) problem is an important issue in social network, which is to seek k nodes with maximal influence cascade such that the influence spread invoked by the k nodes in the network is maximized. The traditional approaches for resolving influence maximization, including Greedy, Distance, DegreeDiscount and PageRank, usually suffer from several drawbacks, such as high computational cost and unstable accuracy. In this paper, we propose a new optimization model, i.e., complete-three-layer-influence evaluation (CTLI), based on an improved three-degree model by considering the intra-layer and inter-layer’s communication effect. A discrete bacterial foraging optimization algorithm is proposed to optimize CTLI model. In this algorithm, the update and mutation rules for the bacteria are redefined to improve the search ability. Finally, the proposed model and algorithm are tested on four real-world social network instances. Results demonstrate that the proposed method outperforms its compared algorithms in terms of solution accuracy and computation efficiency.
Supported by the National Natural Science Foundation of China under Grant No. 61773103, the Fundamental Research Funds for the Central Universities No. N180408019 and N181713002, and the Program for Liaoning Innovative Research Team in University under Grant No. LT2016007.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant No. 61773103 and 61503373, the Fundamental Research Funds for the Central Universities No. N180408019 and N181713002, and the Program for Liaoning Innovative Research Team in University under Grant No. LT2016007.
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Zhang, T., Ma, L., Shi, M. (2020). Evolutionary Optimization of Three-Degree Influence Spread in Social Networks Based on Discrete Bacterial Foraging Optimization Algorithm. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_7
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