In reality, the structure of most graphs could be noisy, i.e., including some noisy edges or ignoring some edges that exist between nodes in practice. To solve these challenges, a novel differentiable similarity module (DSM), which boosted node representations by digging implict association between nodes to improve the accuracy of node classification, was presented. Basic representation of each target node was learnt by DSM using an ordinary graph neural network (GNN), similar node sets were selected in terms of node representation similarity and the basic representation of the similar nodes was integrated to boost the target node’s representation. Mathematically, DSM is differentiable, so it is possible to combine DSM as plug-in with arbitrary GNNs and train them in an end-to-end fashion. DSM enables to exploit the implicit edges between nodes and make the learned representations more robust and discriminative. Experiments were conducted on several public node classification datasets. Results demonstrated that with GNNs equipped with DSM, the classification accuracy can be significantly improved, for example, GAT-DSM outperformed GAT by significant margins of 2.9% on Cora and 3.5% on Citeseer.