In Alzheimer's disease (AD), structural and functional brain network organization is disturbed. However, many of the present network analysis measures require a priori assumptions and methodological choices that influence outcomes and interpretations. Graph spectral analysis (GSA) is a more direct algebraic method that describes network properties, which might lead to more reliable results. In this study, GSA was applied to magnetoencephalography (MEG) data to explore functional network integrity in AD. Sensor-level resting-state MEG was performed in 18 Alzheimer patients (age 67 ± 9, 6 women) and 18 healthy controls (age 66 ± 9, 11 women). Weighted, undirected graphs were constructed based on functional connectivity analysis using the Synchronization likelihood, and GSA was performed with a focus on network connectivity, synchronizability, and node centrality. The main outcomes were a global loss of network connectivity and altered synchronizability in most frequency bands. Eigenvector centrality mapping confirmed the hub status of the parietal areas, and demonstrated a low centrality of the left temporal region in the theta band in AD patients that was strongly related to the mini mental state examination (global cognitive function test) score (r=0.67, p=0.001). Summarizing, GSA is a theoretically solid approach that is able to detect the disruption of functional network topology in AD. In addition to the previously reported overall connectivity losses and parietal area hub status, impaired network synchronizability and a clinically relevant left temporal centrality loss were found in AD patients. Our findings imply that GSA is valuable for the purpose of studying altered brain network topology and dynamics in AD.