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.
Automatic Network Fingerprinting through Single-Node Motifs
C. Echtermeyer,L. D. Costa,F. Rodrigues,Marcus Kaiser
Published 2011 in PLoS ONE
ABSTRACT
PUBLICATION RECORD
- Publication year
2011
- Venue
PLoS ONE
- Publication date
2011-01-31
- Fields of study
Biology, Medicine, Physics, Computer Science
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
CITATION MAP
EXTRACTION MAP
CLAIMS
- No claims are published for this paper.
CONCEPTS
- No concepts are published for this paper.
REFERENCES
Showing 1-72 of 72 references · Page 1 of 1
CITED BY
Showing 1-18 of 18 citing papers · Page 1 of 1