Photo-sharing services have attracted millions of people and helped construct massive social networks on the Web. A popular trend is that users share their image collections within social groups, which greatly promotes the interactions between users and expands their social networks. Existing systems have difficulties in generating satisfactory social group suggestions because the images are classified independently and their relationship in a collection is ignored. In this work, we intend to produce suggestions of suitable photo-sharing groups from a user's personal photo collection by mining images on the Web and leveraging the collection context. Both visual content and textual annotations are integrated to generate initial prediction of the events or topics depicted in the images. A user collection-based label propagation method is proposed to improve the group suggestion by modeling the relationship of images in the same collection as a sparse weighted graph. Experiments on real user images and comparisons with the state-of-the-art techniques demonstrate the effectiveness of the proposed approaches.