242 research outputs found
Automatic Network Fingerprinting through Single-Node Motifs
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.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures
Patterns of Interactions in Complex Social Networks Based on Coloured Motifs Analysis
Coloured network motifs are small subgraphs that enable to discover and interpret the patterns of interaction within the complex networks. The analysis of three-nodes motifs where the colour of the node reflects its high – white node or low – black node centrality in the social network is presented in the paper. The importance of the vertices is assessed by utilizing two measures: degree prestige and degree centrality. The distribution of motifs in these two cases is compared to mine the interconnection patterns between nodes. The analysis is performed on the social network derived from email communication
A Fast Counting Method for 6-motifs with Low Connectivity
A -motif (or graphlet) is a subgraph on nodes in a graph or network.
Counting of motifs in complex networks has been a well-studied problem in
network analysis of various real-word graphs arising from the study of social
networks and bioinformatics. In particular, the triangle counting problem has
received much attention due to its significance in understanding the behavior
of social networks. Similarly, subgraphs with more than 3 nodes have received
much attention recently. While there have been successful methods developed on
this problem, most of the existing algorithms are not scalable to large
networks with millions of nodes and edges.
The main contribution of this paper is a preliminary study that genaralizes
the exact counting algorithm provided by Pinar, Seshadhri and Vishal to a
collection of 6-motifs. This method uses the counts of motifs with smaller size
to obtain the counts of 6-motifs with low connecivity, that is, containing a
cut-vertex or a cut-edge. Therefore, it circumvents the combinatorial explosion
that naturally arises when counting subgraphs in large networks
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