Automatic Network Fingerprinting through Single-Node Motifs

TL;DR

This paper improves and automates a method for identifying characteristic nodes in complex networks, enabling high-throughput analysis and revealing nodes that may be critical to network function.

Contribution

The authors enhance an existing node-motif detection method with automatic parameter determination and validate it across various networks and network time-series.

Findings

Automated routines facilitate high-throughput network analysis.

The method reliably identifies characteristic nodes in diverse networks.

Special nodes may play critical roles in real-world networks.

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.