In addition, we have recently designed a graphlet-based measure of network topology; graphlets are small induced subgraphs of a large network [46, 47] . As opposed to partial subgraphs (e.g., network motifs [48] ), graphlets are induced, meaning that they contain all edges between the nodes of the subgraph that are present in the large network. This measure generalizes the degree of a node that counts the number of edges that the node touches, where an edge is the only 2-node subgraph, into the graphlet degree vector (GDV) that counts the number of different graphlets that the node touches, for all 2-5-node graphlets. Hence, GDV of a node describes the topology of its up to 4-deep neighborhood. This is an effective measure: going to distance of 4 around a node captures a large portion of a network due to the small-world nature of many real networks [49] . For this reason, and since the number of graphlets on n nodes increases exponentially with n, we believe that using larger graphlets would unnecessarily increase the computational complexity of the method. We designed the similarity measure between GDVs of different nodes, GDVsimilarity, to quantify the topological similarity of the extended neighborhoods of two nodes. We used this constraining measure of network topological similarity to demonstrate that: in PPI networks, biological function of a protein and its local network structure are closely related [27, 50] ; from topology of PPI networks we can extract biological information that cannot always be extracted from sequence and hence, topology could be used as a complementary method to sequence-based methods for homology detection [51] ; topology around cancer and non-cancer genes is different and can be used to successfully predict new cancer genes in melanogenesis-related pathways [31, 40] ; purely topological network alignments can be used to extract protein function and species phylogeny [52, 53] .