Abstract
Identifying the influential spreaders is an important issue in understanding and controlling the spreading processes in social networks. The key to the influential spreader identification problem is how to evaluate the spreading ability of the nodes. Centralities such as degree, semi-local centrality, betweenness, closeness, k-shell are usually used as evaluation metric. However, we observe that these centralities are sensitive not only to the spreading probability, but also to the network structure. We are not sure which centrality is efficient when we face a new network. In this paper, we propose a robust method named Adjustable multi-Hops Spreading (AHS). In AHS, we refine the influence of a node into direct influence and indirect influence, and then integrate them with a adjustable parameter. The experimental results in both real social networks and artificial networks show that AHS outperforms other centralities in effectiveness, robustness and the distinguish ability.
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Acknowledgements
This work was funded by the Natural Science Foundation of China (61672322, 61672324, 61272240), the Natural Science Foundation of Shandong province (2016ZR E27468, ZR2012FM037) and the Fundamental Research Funds of Shandong University.
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Author Qian Ma declares that she has no conflict of interest. Author Jun Ma declares that he has no conflict of interest.
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Ma, Q., Ma, J. A robust method to discover influential users in social networks. Soft Comput 23, 1283–1295 (2019). https://doi.org/10.1007/s00500-017-2847-5
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DOI: https://doi.org/10.1007/s00500-017-2847-5