Entity alignment (EA) aims to identify equivalent entities from different Knowledge Graphs (KGs), which is a fundamental task for integrating KGs. Throughout its development, Graph Convolutional Network (GCN) has become one of the mainstream methods for EA. These GCN-based methods learn the representations of entities from two KGs by message passing mechanism and then make alignments via measuring the similarity between entity embeddings. The key idea that GCN works in EA is that entities with similar neighbor structures are highly likely to be aligned. However, the noisy neighbors of entities transfer invalid information, drown out equivalent information, lead to inaccurate entity embeddings, and finally reduce the performance of EA. Based on the Sinkhorn algorithm, we design a reliability measure for potential equivalent entities and propose Adaptive Graph Convolutional Network to deal with neighbor noises in GCN. During the training, the network dynamically updates the adaptive weights of relation triples to weaken the propagation of noises. While calculating entity similarity, it comprehensively considers the self-similarity and neighborhood similarity of the entity pair to alleviate the influence of noises. Furthermore, we design a straightforward but efficient strategy to construct pseudo alignments for unsupervised EA. Extensive experiments on benchmark datasets demonstrate that our framework outperforms the state-of-the-art methods in both supervised and unsupervised settings.