Previously, we defined GDV-similarity of nodes' neighborhoods that is independent of the densities of these neighborhoods: nodes with identical GDVs have the maximum GDV-similarity, regardless of whether they reside in dense or sparse neighborhoods. Here, we propose a new centrality measure, graphlet degree centrality (GDC), to measure the density and complexity of nodes' neighborhoods by counting the number of different graphlets that the node touches. According to GDC, nodes in dense and complex 4-deep neighborhoods will have higher centralities than nodes in sparse 4-deep neighborhoods. GDC is a different and more constraining measure of network topology than the degree centrality (DC), as illustrated in Figure 1 : GDC ranks highly a low-degree gene if its 4-deep neighborhood is dense and gives a low rank to a high-degree gene if its 4-deep neighborhood is sparse (details are below). GDC is conceptually different than the betweenness centrality (BWC), which does not measure topological denseness at all. Subgraph centrality (SC) measures the number of closed walks (which can be thought of as partial subgraphs) that the node touches and it has been shown to be more highly correlated with the lethality of proteins in the PPI network of baker's yeast than DC [44] . Unlike SC, GDC counts induced subgraphs rather than partial ones and in a more rigorous way: while SC counts an edge that a node touches many times, as a 2-edge closed walk (going from node A to node B along edge AB and returning from B to A along the same edge), as a 4-edge closed walk (going from node A to node B and back to A twice), as a 6edge closed walk (going from A to B and back to A three times) etc., GDC counts the edge only once and only as an edge, rather than as different subgraph structures.