Abstract
Recently, the issue of maximizing the influence of social networks is a hot topic. In large-scale social networks, the mining algorithm for maximizing influence seed nodes has made great progress, but only using influence as the evaluation criterion of seed nodes is not enough to reflect the quality of seed nodes. This paper proposes an Out-degree Graph Clustering algorithm (OGC algorithm) to dynamically select the out-degree boundary to optimize the range of clustering. On this basis, we propose an Adaptive Seed node Mining algorithm based on Out-degree (ASMO algorithm). Experiments show that our algorithm keeps the balance between the cost and benefit of seed node mining, and greatly shortens the running time of seed node mining.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 61701104), and by the Science and Technology Development Plan of Jilin Province, China (No.20190201194JC).
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Zhou, T.H., Jiang, B., Lu, Y., Wang, L. (2020). An Adaptive Seed Node Mining Algorithm Based on Graph Clustering to Maximize the Influence of Social Networks. In: Huang, DS., Premaratne, P. (eds) Intelligent Computing Methodologies. ICIC 2020. Lecture Notes in Computer Science(), vol 12465. Springer, Cham. https://doi.org/10.1007/978-3-030-60796-8_43
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DOI: https://doi.org/10.1007/978-3-030-60796-8_43
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