In a diffusion network, some nodes exhibit similar diffusion patterns as they have analogous influence reachabilities to the other nodes. When these nodes are selected as initially infected nodes, they tend to yield similar infection results. Mining diffusion patterns of nodes is of practical significance in various applications, such as online marketing and epidemic prevention. Nonetheless, few existing work has effectively addressed this problem. In this work, we investigate how to find out which nodes in a diffusion network share similar diffusion pattern based only on historical infection results. Towards this, we first reconstruct the structure of influence relationships in the network, and then infer the infection propagation probability on each influence relationship, based on which the influence reachability of each node can be estimated. We present a diffusion pattern similarity metric to quantify the similarity of influence reachabilities, and group nodes that share similar influence reachabilities via hierarchical clustering. Extensive experimental results on both synthetic and real-world networks verify the effectiveness and efficiency of our approach.