Recently, network motif (or graphlet) properties have been widely utilized as important topological features of bio-networks. Network motifs are recurrent and statistically significant partial subgraphs or patterns. In this thesis, we analyzed various bio-networks based on topological property of network motifs. In part I, we developed Typed Network Motif Comparison Algorithm (TNMCA) for repositioning drugs using topological properties of given networks. TNMCA is a powerful inference algorithm for multi-level biomedical interaction data as the algorithm depends on the different types of entities and relations. In part II, we propose a new network model incorporating grouped attachment (GA) and apply it to real-world networks. Corresponding networks generated by GA model showed a higher similarity of motif properties with real-world networks compared to corresponding networks generated by existing network models.