講座內(nèi)容：Many scientific systems of interests can be modeled as a network. On the other hand, the best way to understand the specialty and the importance of an individual is placing it into a propernetwork. A network is representedas an (un)directed graph,where nodes or vertices stand for individuals while edges or arcs for the relationship between pairs of nodes. Static network analysis is to identifyimportant elements (node or edge) in networks, based on only the topology of networks. To measure the importance of an element (node or edge), various centrality metrics have been proposed in past years. In this talk, after reviewing several commonly used centrality metrics I will present some applications of static network analysis in identification of essential proteins, drug targets and disease genes from biomolecular networks.