Chronic infections with the hepatitis B virus (HBV) and hepatitis C virus (HCV) are the major risks of hepatocellular carcinoma (HCC), and great efforts have been made towards the understanding of the different mechanisms that link the viral infection of hepatic lesions to HCC development. In this work, we developed a novel framework to identify distinct patterns of gene coexpression networks and inflammation-related modules from genome-scale microarray data upon viral infection, and further classified them into oncogenic and dysfunctional ones. The core of our framework lies in the comparative study on viral infection modules across different disease stages and disease types—the module preservation during disease progression is evaluated according to the change of network connectivity in different stages, while the similarity and difference in HBV and HCV are evaluated by comparing the overlap of gene compositions and functional annotations in HBV and HCV modules. In particular, we revealed two types of driving modules related to infection for carcinogenesis in HBV and HCV, respectively, i.e. pro-apoptosis modules that are oncogenic in HBV, and anti-apoptosis and inflammation modules that are oncogenic in HCV, which are in concordance with the results of previous differential expression-based approaches. Moreover, we found that intracellular protein transmembrane transportation and the transmembrane receptor protein tyrosine kinase signaling pathway act as oncogenic factors in HBV-HCC. Our findings provide novel insights into viral hepatocarcinogenesis and disease progression, and also demonstrate the advantages of an integrative and comparative network analysis over the existing differential expression-based approach and virus–host interactome-based approach.