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Link prediction in attributed networks has attracted increasing attention recently due to its valuable real-world applications. Various related methods have been proposed, but most of them cannot effectively utilize community structure, neither can they well fuse attribute information and link information to improve the performance. Inspired by our empirical observations on how community structure affects the generation of links, we propose a novel Community Preserving Adaptive Graph Convolutional Networks (CPAGCN) method to tackle the link prediction task in attributed networks. Specifically, CPAGCN is composed of two core modules: network embedding and link prediction. Network embedding module utilizes AGCN to seamlessly fuse link information and attribute information to obtain node representations, which are simultaneously driven to preserve community structure via an appropriate community detection model. Taking these node representations as the input, link prediction module applies multilayer perception (MLP) to directly learn the prediction scores for potential links. Through combining the graph reconstruction loss with the prediction loss to train AGCN and MLP jointly, CPAGCN can learn node representations that are more beneficial to predicting links. To verify the effectiveness of CPAGCN, we conduct extensive experiments on six real-world attributed networks. The results demonstrate that CPAGCN performs better than several strong competitors in link prediction. The source code is available at https://github.com/GDM-SCNU/CPAGCN. • We investigate the impact of community structure on links by empirical observations. • An end-to-end method CPAGCN for link prediction in attributed networks is proposed. • CPAGCN can seamlessly fuse attribute and structural proximities. • CPAGCN uses community preserving network embedding to boost link prediction. • CPAGCN outperforms state-of-the-art baselines. [ABSTRACT FROM AUTHOR]