Discovery of disease biomarkers is a key step in translating advances in genomics into clinical practice. There is growing evidence that changes in gut microbial composition are associated with the onset and progression of Type 2 Diabetes (T2D), Obesity, and Inflammatory Bowel Disease (IBD). Reliable identification of the most informative features (i.e., microbes) for discriminating metagenomics samples from two or more groups (i.e., phenotypes) is a major challenge in computational metagenomics. We propose a Network-Based Biomarker Discovery (NBBD) framework for detecting disease biomarkers from metagenomics data. NBBD has two major customizable modules: i) A network inference module for inferring ecological networks from the abundances of microbial operational taxonomic units (OTUs); ii) A node importance scoring module for comparing the constructed networks for the chosen phenotypes and assigning a score to each node based on the degree to which the topological properties of the node differ across two networks. We empirically evaluated the proposed NBBD framework, using five network inference methods for inferring gut microbial networks combined with six node topological properties, on the identification of IBD biomarkers using a large dataset from a cohort of 657 and 316 IBD and healthy controls metagenomic biopsy samples, respectively. Our results show that NBBD is very competitive with some of the state-of-the-art feature selection methods including the widely used method based on random forest variable importance scores.