Title: Generalized structure-aware missing view completion network for incomplete multi-view clustering. Abstract: In recent years, incomplete multi-view clustering has been widely regarded as a challenging problem. The missing views inevitably damage the effective information of the multi-view data itself. To date, existing methods for incomplete multi-view clustering usually bypass invalid views according to prior missing information, which is considered as a second-best scheme based on evasion. Other methods that attempt to recover missing information are mostly applicable to specific two-view datasets. To handle these problems, we design a general structure-aware missing view completion network (SMVC) for incomplete multi-view clustering. Concretely, we build a two-stage autoencoder network with the self-attention structure to synchronously extract high-level semantic representations of multiple views and recover the missing data. In addition, we develop a recurrent graph reconstruction mechanism that cleverly leverages the restored views to promote the representation learning and the further data reconstruction. Sufficient experimental results confirm that our SMVC has obvious advantages over other top methods.