Identifying the influential spreaders is an impor-tant issue in understanding and controlling the spreading pro-cesses 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 cen-
trality, betweenness, closeness, k-shell are usually used as evaluation metric. However, we observe that these central-ities are sensitive not only to the spreading probability, but also to the network structure. We are not sure which central-ity 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, robust-ness and the distinguish ability.

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