Information, disease, and influence diffuse over networks of entities in both natural systems and human society. Analyzing these transmission networks plays an important role in understanding the diffusion processes and predicting events in the future. However, the underlying transmission networks are often hidden and incomplete, and we observe only the time stamps when cascades of events happen. In this paper, we attempt to address the challenging problem of uncovering the hidden network only from the cascades. The structure discovery problem is complicated by the fact that the influence among different entities in a network are heterogeneous, which can not be described by a simple parametric model. Therefore, we propose a kernel-based method which can capture a diverse range of different types of influence without any prior assumption. In both synthetic and real cascade data, we show that our model can better recover the underlying diffusion network and drastically improve the estimation of the influence functions between networked entities.