Sparse representation-based brain network modeling, although popular, often results in relatively large inter-subject variability in network structures. This inevitably makes it difficult for inter-subject comparison, thus eventually deteriorating the generalization capability of personalized disease diagnosis. Accordingly, group sparse representation has been proposed to alleviate such limitation by jointly estimating connectivity weights for all subjects. However, the constructed brain networks based on this method often fail in providing satisfactory separability between the subjects from different groups (e.g., patients vs. normal controls), which will also affect the performance of computer-aided disease diagnosis. Based on the hypothesis that subjects from the same group should have larger similarity in their functional connectivity (FC) patterns than subjects from other groups, we propose an “inter-subject FC similarity-guided” group sparse network modeling method. In this method, we explicitly include the inter-subject FC similarity as a constraint to conduct group-wise FC network modeling, while retaining sufficient between-group differences in the resultant FC networks. This improves the separability of brain functional networks between different groups, thus facilitating better personalized brain disease diagnosis. Specifically, the inter-subject FC similarity is roughly estimated by comparing the Pearson’s correlation based FC patterns of each brain region to other regions for each pair of the subjects. Then, this is implemented as an additional weighting term to ensure the adequate inter-subject FC differences between the subjects from different groups. Of note, our method retains the group sparsity constraint to ensure the overall consistency of the resultant individual brain networks. Experimental results show that our method achieves a balanced trade-off by not only generating the individually consistent FC networks, but also effectively maintaining the necessary group difference, thereby significantly improving connectomics-based diagnosis for mild cognitive impairment (MCI).