Brain network plays an important role in representing abnormalities in Alzheimers disease (AD) and mild cognitive impairment (MCI), which includes MCIc (MCI converted to AD) and MCInc (MCI not converted to AD). In our previous study, we proposed an AD classification approach based on individual hierarchical networks constructed with 3D texture features of brain images. However, we only used edge features of the networks without node features of the networks. In this paper, we propose a framework of the combination of multiple kernels to combine edge features and node features for AD classification. An evaluation of the proposed approach has been conducted with MRI images of 710 subjects (230 health controls (HC), 280 MCI (including 120 MCIc and 160 MCInc), and 200 AD) from the Alzheimer's disease neuroimaging initiative database by using ten-fold cross validation. Experimental results show that the proposed method is not only superior to the existing AD classification methods, but also efficient and promising for clinical applications for the diagnosis of AD via MRI images. Furthermore, the results also indicate that 3D texture could detect the subtle texture differences between tissues in AD, MCI, and HC, and texture features of MRI images might be related to the severity of AD cognitive impairment. These results suggest that 3D texture is a useful aid in AD diagnosis.