Currently, the number of patients with COVID-19 has significantly increased. Thus, there is an urgent need for developing treatments for COVID-19. Drug repurposing, which is the process of reusing already-approved drugs for new medical conditions, can be a good way to solve this problem quickly and broadly. Many clinical trials for COVID-19 patients using treatments for other diseases MESHD have already been in place or will be performed at clinical sites in the near future. Additionally, patients with comorbidities such as diabetes mellitus MESHD diabetes mellitus HP, obesity MESHD obesity HP, liver cirrhosis MESHD cirrhosis HP, kidney diseases MESHD, hypertension MESHD hypertension HP, and asthma MESHD asthma HP are at higher risk for severe illness from COVID-19. Thus, the relationship of comorbidity disease MESHD with COVID-19 may help to find repurposable drugs. To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs. First, we utilized knowledge of COVID-19 to construct a disease MESHD-gene-drug network (DGDr-Net) representing a COVID-19-centric interactome with components for diseases MESHD, genes, and drugs. DGDr-Net consisted of 592 diseases MESHD, 26,681 human genes and 2,173 drugs, and medical information for 18 common comorbidities. The DGDr-Net recommended candidate repurposable drugs for COVID-19 through network reinforcement driven scoring algorithms. The scoring algorithms determined the priority of recommendations by utilizing graph-based semi-supervised learning. From the predicted scores, we recommended 30 drugs, including dexamethasone, resveratrol, methotrexate, indomethacin, quercetin, etc., as repurposable drugs for COVID-19, and the results were verified with drugs that have been under clinical trials. The list of drugs via a data-driven computational approach could help reduce trial-and-error in finding treatment for COVID-19.