We have been witnessing the exponential growth of the amounts of available PPI network data, along with the development of computational approaches for studying and modeling of these data. High-throughput screens for interaction bacterial or viral pathogens (e.g., HIV, herpesvirus, hepatitis, and influenza), exhibit some ''topological centrality'' compared to the rest of the proteins in the PPI network [1, [28] [29] [30] [31] [33] [34] [35] . Many approaches have focused on examining only simple topological properties of these proteins, such as their direct neighborhoods in a PPI network. For example, the key assumption of many studies is that proteins that are direct neighbors are more likely to perform the same function than those that are not [25, 26] , or that a neighbor of a disease-causing gene is likely to cause either the same or a similar disease [1, 34] . Another example is the observed correlation between a protein's essentiality and its degree centrality (the larger the degree of a node, the more ''degree-central'' the node) in a PPI network of baker's yeast [36] . However, the controversy arose in the light of newer and more complete PPI network data for which this correlation was not observed [37, 38] and it appears to hold only for literature-curated [39] and smaller in scope Y2H PPI networks [3] , possibly because these data sets are biased towards essential proteins [38] . Also, degree alone might be a weak measure of network topology, as it captures limited network topology, i.e., only direct neighborhood of a node [27, 31, 40] . A similar controversy arose when cancer genes were initially shown to have greater connectivities and centralities compared to non-cancer genes, indicating central roles of cancer genes within the interactome [33] , but it was later demonstrated that most of disease genes do not show a tendency to code for proteins that are hubs [29] , although a recent study again reached the conclusion that cancer proteins have different network topologies, e.g., higher degrees, than ''control'' genes [35] . Apart from this, general conclusions are that disease genes have high connectivity and are centrally positioned within the PPI network [1] . In addition, it has been suggested that aging genes tend to have higher degrees than non-aging ones [41, 42] , as well as that the majority of viral and bacterial pathogens show tendency to interact with high-degree proteins, or with ''bottleneck'' proteins that are central to many paths in the PPI network [43] .