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, etweenness, 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.