title: Automatic network fingerprinting through single-node motifs.
abstract: Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs-a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.
authors.affiliation: School of Computing Science, Newcastle University, Newcastle-upon-Tyne, United Kingdom.
authors.forename: Christoph
authors.initials: C
authors.lastname: Echtermeyer
authors.forename: Luciano da Fontoura
authors.initials: Lda F
authors.lastname: Costa
authors.forename: Francisco A
authors.initials: FA
authors.lastname: Rodrigues
authors.forename: Marcus
authors.initials: M
authors.lastname: Kaiser
mesh_terms: D000465:Algorithms; D057166:High-Throughput Screening Assays; D008722:Methods; D008954:Models, Biological; D008962:Models, Theoretical